{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Molecules With a Self-Organizing Map (SOM)\n",
    "This notebook provides an overview of the use of self-organizing maps (SOMs) in cheminformatics. For a bit of background on SOMs, please see this [blog post](http://practicalcheminformatics.blogspot.com/2018/10/self-organizing-maps-90s-fad-or-useful.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the necessary Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:24.599332Z",
     "start_time": "2025-05-06T11:35:24.591642Z"
    }
   },
   "source": [
    "%%capture\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "if IN_COLAB:\n",
    "    !pip install minisom tqdm rdkit_pypi mols2grid"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:27.324300Z",
     "start_time": "2025-05-06T11:35:27.272981Z"
    }
   },
   "source": [
    "from collections import Counter\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, MACCSkeys\n",
    "from rdkit import DataStructs\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "from minisom import MiniSom\n",
    "import sys\n",
    "from time import time\n",
    "import mols2grid\n",
    "from ipywidgets import interact\n",
    "from rdkit import rdBase"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enable matplotlib plots in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:30.112606Z",
     "start_time": "2025-05-06T11:35:30.097971Z"
    }
   },
   "source": [
    "%matplotlib inline"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining a Few Useful Functions\n",
    "A few functions to generate fingerprints. The first function generates 166-bit MACCS keys.  The second generates Morgan fingerprints.  While both will work for building a SOM, the process will be a bit faster with MACCS keys. I tend to like MACCS keys for generating SOMs.  These fingerprints typically do a good job of grouping a set of molecules by scaffold. The third function takes a list of SMILES as input and returns as a list of fingerprints. If this function is called with one argument, it generates MACCS keys.  We can also pass a function as a second argument to generate a different fingerprint type.  For instance, we could call it like this to generate Morgan fingerprints.  \n",
    "```\n",
    "generate_fps(my_smiles_list,morgan_as_np)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:33.597658Z",
     "start_time": "2025-05-06T11:35:33.593325Z"
    }
   },
   "source": [
    "def maccs_as_np(mol):\n",
    "    \"\"\"\n",
    "    Generate MACCS fingerprints as a NumPy array\n",
    "    :param mol: input molecule\n",
    "    :return: fingerprint as a NumPy array\n",
    "    \"\"\"\n",
    "    bv = MACCSkeys.GenMACCSKeys(mol)\n",
    "    return np.array([int(x) for x in list(bv.ToBitString())], dtype=np.float32)\n",
    "\n",
    "\n",
    "def morgan_as_np(mol):\n",
    "    \"\"\"\n",
    "    Generate a 1024 bit Morgan fingerprint as a NumPy array\n",
    "    :param mol: input molecule\n",
    "    :return: fingerprint as a NumPy array\n",
    "    \"\"\"\n",
    "    bv = AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=1024)\n",
    "    arr = np.zeros((1,), dtype=np.float32)\n",
    "    DataStructs.ConvertToNumpyArray(bv, arr)\n",
    "    return arr\n",
    "\n",
    "\n",
    "def generate_fps(smiles_list, fp_function=maccs_as_np):\n",
    "    \"\"\"\n",
    "    Take a list of SMILES as input and return a list of NumPy arrays\n",
    "    :param smiles_list: list of SMILES\n",
    "    :param fp_function: function to calculate fingerprints\n",
    "    :return: list of NumPy arrays containing fingerprints\n",
    "    \"\"\"\n",
    "    output_fp_list = []\n",
    "    for smiles in tqdm(smiles_list, desc=\"Generating Fingerprints\"):\n",
    "        output_fp_list.append(fp_function(Chem.MolFromSmiles(smiles)))\n",
    "    return output_fp_list"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function to generate a grid of pie charts showing the distribution of active and inactive compounds in each grid cell. "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:39.627707Z",
     "start_time": "2025-05-06T11:35:39.623364Z"
    }
   },
   "source": [
    "# Adapted from the MiniSom example notebook\n",
    "def depict_som(cluster_df, x_dim, y_dim, x_column=\"X\", y_column=\"Y\", activity_column=\"is_active\"):\n",
    "    \"\"\"\n",
    "    Draw a SOM with each cell depicted as a pie chart\n",
    "    :param cluster_df: data frame with SOM output, should have columns active, X, and Y\n",
    "    :param x_dim: X dimension of the SOM\n",
    "    :param y_dim: Y dimension of the SOM\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    required_colums = [x_column, y_column, activity_column]\n",
    "    for col in required_colums:\n",
    "        if col not in cluster_df.columns:\n",
    "            print(f\"Error {col} not in dataframe columns\", file=sys.stderr)\n",
    "            sys.exit(1)\n",
    "    cell_dict = {}\n",
    "    for k, v in [x for x in cluster_df.groupby([x_column, y_column])]:\n",
    "        cell_dict[k] = Counter(v[activity_column])\n",
    "        cell_names = cluster_df[activity_column].unique()\n",
    "    plt.figure(figsize=(x_dim, y_dim))\n",
    "    the_grid = GridSpec(x_dim, y_dim)\n",
    "    for position in cell_dict.keys():\n",
    "        label_fracs = [cell_dict[position][x] for x in cell_names]\n",
    "        plt.subplot(the_grid[(x_dim - 1) - position[1], position[0]], aspect=1)\n",
    "        patches, texts = plt.pie(label_fracs)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a SOM with minisom"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:43.240955Z",
     "start_time": "2025-05-06T11:35:43.237178Z"
    }
   },
   "source": [
    "def build_minisom_som(fp_list_in, x_dim=10, y_dim=10, num_iters=20000):\n",
    "    \"\"\"\n",
    "    Build a SOM with MiniSom\n",
    "    :param fp_list_in: input list of fingerprints as NumPy arrays\n",
    "    :param x_dim: X dimension of the SOM\n",
    "    :param y_dim: Y dimension of the SOM\n",
    "    :param num_iters: number of iterations when building the SOM\n",
    "    :return: lists with X and Y coordinates in the SOM\n",
    "    \"\"\"\n",
    "    print(\"Training SOM\")\n",
    "    start_time = time()\n",
    "    som = MiniSom(x_dim, y_dim, len(fp_list_in[0]), sigma=0.3, learning_rate=0.5, random_seed=1)\n",
    "    som.train_random(fp_list_in, num_iters)\n",
    "    x = []\n",
    "    y = []\n",
    "    # find best matching units\n",
    "    print(\"Finding BMUs\")\n",
    "    for row in fp_list_in:\n",
    "        x_val, y_val = som.winner(row)\n",
    "        x.append(x_val)\n",
    "        y.append(y_val)\n",
    "    elapsed_time = time()-start_time\n",
    "    print(\"Done\\nElapsed time = %.2f sec\" % elapsed_time)\n",
    "    return x, y"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading the Input Data\n",
    "Now that we have the necessary functions in place, we can have some fun.  We will read in a csv file containing SMILES, a molecule name, and 1 or 0 indicating that the molecule is active into a Pandas data frame. \n",
    "\n",
    "We then use the function generate_fps to generate a list of fingerprints from the SMILES column in the dataframe. This list of fingerprints is then used to generate X and Y coordinates for each molecule in the grid.  The x and y coordinates generated by build_minisom_som are then added as columns to the original dataframe.  This dataframe, as well as the grid dimensions, are then passed to the depiction function to generate the plot below. "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:45.953004Z",
     "start_time": "2025-05-06T11:35:45.687514Z"
    }
   },
   "source": [
    "act_df = pd.read_csv(\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/main/data/dude_erk2_mk01.csv\")\n",
    "act_df.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Unnamed: 0                                             SMILES      ID  \\\n",
       "0           0  Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCC...  168691   \n",
       "1           1  C[C@@]12[C@@H]([C@@H](CC(O1)n3c4ccccc4c5c3c6n2...   86358   \n",
       "2           2  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  575087   \n",
       "3           3  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  575065   \n",
       "4           4  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  575047   \n",
       "\n",
       "   is_active  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  "
      ],
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       "</table>\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding Labels\n",
    "Active and decoy are currently indicated by 1 and 0 in the dataframe.  To have better labels, we will convert 1 and 0 to the strings **active** and **decoy** and add new column called **label**."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:54.167200Z",
     "start_time": "2025-05-06T11:35:54.163382Z"
    }
   },
   "source": [
    "act_df['label'] = [\"active\" if i == 1 else \"inactive\" for i in act_df.is_active]"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:55.877297Z",
     "start_time": "2025-05-06T11:35:55.873387Z"
    }
   },
   "source": [
    "act_df.label.value_counts()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "inactive    4550\n",
       "active        79\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:35:57.316862Z",
     "start_time": "2025-05-06T11:35:57.312156Z"
    }
   },
   "source": [
    "act_df.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Unnamed: 0                                             SMILES      ID  \\\n",
       "0           0  Cn1ccnc1Sc2ccc(cc2Cl)Nc3c4cc(c(cc4ncc3C#N)OCCC...  168691   \n",
       "1           1  C[C@@]12[C@@H]([C@@H](CC(O1)n3c4ccccc4c5c3c6n2...   86358   \n",
       "2           2  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  575087   \n",
       "3           3  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  575065   \n",
       "4           4  Cc1cnc(nc1c2cc([nH]c2)C(=O)N[C@H](CO)c3cccc(c3...  575047   \n",
       "\n",
       "   is_active   label  \n",
       "0          1  active  \n",
       "1          1  active  \n",
       "2          1  active  \n",
       "3          1  active  \n",
       "4          1  active  "
      ],
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building and Displaying the SOM\n",
    "Now we'll generate fingerprints for the molecules, the build and display the SOM.\n",
    "\n",
    "The dataset we used above is the [ERK2 (aka MK01)](http://dude.docking.org/targets/mk01) dataset that is part of the [DUD-E dataset](http://dude.docking.org/), which was designed for the evaluation of docking programs.  The DUDE-E database consists of sets of active compounds, curated from the literature, and decoy compounds with similar calculated properties (molecular weight, LogP).  The compound sets in the database were designed to evaluate the ability of a docking program to distinguish active compounds from decoys.  \n",
    "\n",
    "In the plot below, the active compounds are shown in blue, while the decoy compounds are shown in orange.  As we can see, our fingerprints do a reasonably good job of separating the active compounds from the decoys.  In particular, we see that one cell at position 6,4 (we start counting from 0) is highly enriched in active compounds.  Let's take a closer look at molecules in that cell.  "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:36:07.212650Z",
     "start_time": "2025-05-06T11:36:02.242646Z"
    }
   },
   "source": [
    "with rdBase.BlockLogs():\n",
    "    morgan_list = generate_fps(act_df.SMILES,morgan_as_np)\n",
    "x_dim = 10\n",
    "y_dim = 10\n",
    "morgan_x, morgan_y = build_minisom_som(morgan_list, x_dim, y_dim)\n",
    "act_df[\"morgan_X\"] = morgan_x\n",
    "act_df[\"morgan_Y\"] = morgan_y\n",
    "depict_som(act_df, x_dim, y_dim, x_column=\"morgan_X\",y_column=\"morgan_Y\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generating Fingerprints:   0%|          | 0/4629 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "83d8f9e5d48b42df81e2a7092d8ca55f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training SOM\n",
      "Finding BMUs\n",
      "Done\n",
      "Elapsed time = 3.69 sec\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 100 Axes>"
      ],
      "image/png": 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qzYqPZQPpNFwsS6J7US+Zqs2s+2Tt6TRcUml483f65DeWd/6M9miV50i0Nk74vpSy6Gz/6Htyzs17Qt8YGkP5ZnEsdUaXXQcWPhetzmb14atF0mQiRMOlwXguTLtFSuHixIIpsP4LvevOTksnxyhkVhrC+3/X75BbNvz3z3rH0BjevjHUVvzhORp2SjzIOPLhHXrlp3LkEIwj7/1Nr+FSsUU668QNNyO/c12OhudKNH75h3rkN4XNK2D+FH2Gi+vImtuxXo/8pvD5a7DuU41GgwXv/V1/ZqAxTP+P3iYdqbQYT3Hkvb/rfUPIc+Djf+uT31h8vyqgoSmw4ntSdvnZy3rkN4WdG2HmvfoCoW5GupttWqpHflNYMV3u4obkqIXnaOzYAAufCe1zStlSJnWbOhZk0At93afqZYfBF2/ApuV6DAfXkTZ7cctmBMy6X5/BZ6eklCaufPwvfYZLKh2fu1S74qO79Ml2ymHOI/rkN4XyTTD3cT0Oru9JJiNK/fcbQtlMWDVHT5dHNyOP30b9AnhdzH0cMjv1yf/orngGBkCCA7oCoakc+PifemSHwUd3hVYaH46j4TpS5607TdUUZj2gZ0Ha6Wj2pG4IM+/VYzCn0vHMZgTs2ACfvqjHcMnskGhLXFk2DTYtU68AA4Mv6h1/dsfcJ/QZfPOeim5L1vpQ+qA+B3fmfXrkhsWs+/QE5lM58bqHVpvMTr0O7pzH1MsNi7ULYdVs9eed78PmlfFpULMrFj0X2lkdjqORSkurwjjz+Wt6usf4nrwCHmcWPqOnE9CmZfGrV67NgqnqDRfPkXcp4poJCpj3lPrghu/B/KfUygybnRvlUr3q4EAqJ75Z74BlH0iAQDWWHX8du+AZPRebyzfDsvfVyw2ThU+r1xO+D0unxbNEtCbzpwCKzzovI/dr4poJAnFwP30plEqfcHa950XzVdyG4Dl6opQbv4xH27Pdse4z9aldz5EawrhTNkuP3Dh1S6qLslnqla+dhpWafmdhsnKGno4sZQlYdytnqI+Q7lgvD7nFmW1rYJviTmO+J7+vOBt8II12VOM5sHK6erlhUzZLfXfMVG5ydGwI3THDcTQ2fKG3hjAsVs5Qm550nfjW3NbE96RXutLD3JKUaNzZsBgqFWcW7HQy5k7Xz7CqVI/cMFk1W72Ttn2ddFqLO2Wz1D5+6HnJcG5BDFeVDq7n6gvmhMmO9fJYqUpSOUZPxFFumKyaHUq1StMdjaQYyyBRA5Wer2XpiVRkg5UzJF2oCjuVDIPP92H1bPURtyTM3cYl6rOBW1fHv5QA1P/+PTcZ0VGQuVPppPluMjJBUOWkKTzrUjnJ0bErFDtpkIy527lRfTawcocE4ONOSM5S0x0N206G0QLyc6h8RC0pxjJUKd9cxTITEDEA9U7alpXx7cBSm7JSdYaL54qyTwKblkG5wodVfT8ZkWVQb3ylchKmJxSXsSRm7mYBCp20iq0SzEkCKp00XcHDbFC+WbqKNpGmOxqWDRsSshg3LFYvM26P9NWF6rkr3yz/JIENS9Rm0tYnINISsP5zdRfCPTc5ihfU7tlUOjl6YstK9R2AkjJ3qn8OzxWnOgmo1hNJmTeQs06Vo+E5opeSQghPL4RzRyMJ9zNAeryrJilzl1E8d6rlZZPMTnXdWHwfKrepkaUCZyfKonwW8e/UVRPVZ09SzjpQ36EwKXOnWse6GjpJZgvVa0D13cFsktmpsLWyn5z9CqLzmpidCekdDY2vpYaJ76tv+ZiUuVNZ+gPJmTeQCIiqNKvv63tROxt4rsIUtZWwdWf2bKNReRkckjN3qh00Xa9CZwOVegL0BF6zhe+izNPwSc5+BbGJm2gXh+NoqG4dlk1U9/lOytxZit/RSMq8gaw5VXeDLEvPmyfZwrIU3qvyk7fuVGLmrvEkZe6Ut6NO0lmnUE+A+juXWUXxS5FJ2a8ANF3HhnNa5uSH8hntpPM0yEzI3KleAzp+V9kip0BdpMqyIKdQjSwVpAvUyfIVy8s2OS0Uy0vIWQfqjbCkzJ1qfZckYzlH8dmTlDUH6nVsovREQZMDK013NHwfWnZt8mcigY6fo5WZu0aR3zo5zkbLLmpfuG5dpE5WtmnVVV02zbKhVRc1slSg8uzx3OToify26s+epMydan2XyoEW7dXKzBYtu6htb5sU2wRk7pRVDaSgVTc1slTQurjJn2i6o+E5UFTS5M9EAtU/h+9Dt5FqZWaLohK1tf92GjoPVScvmxSNVlt61qF/cjJp3ceqK49IpaH7ODWysk1BO7UOp+8nR0+oPrM9N1lzp/otiG4lauVli6IStXc0WnaBwo7q5GWT7uPUle3ZNhSPViMr26TzoNNeTf5M0x2NVA4Uj23yZyJBtxK1xrLnJOgQVGws+15ylG/xaDmcVGGnoOswdfKyRWFHUYYqaddXfclRNigapVZekpy0ohLFxrKfHD3RrQSlb0EkScd2H6f+DZIkzF06X4JrKinsmIyMUOehodw3Cce66TI0GZeuiker/TlSORKRTQKqjWXPTcYh2Kqr+qiR7yVj7nT8DLYNXYerlxs23UrUlusBtOsNea3UyswGqo1lO0FOWvdxii/KWskISOW0kCCHSpJSrdJ1mB77NAk6tqgklE6s4ViG6TzoODCUT2mlaLT6biKdB8e/Q0HrYinFUElSnDQdh1FSSjGKStQby0nJpBWVoLwTC0DXEeplho1yYxlo2wPy26iVGTa5LaFdL7Uy7VQynLSuw9UG8gCwoJvizGc26Fai4dmChDhp3UpCyd6Gs3I9FwYcHsqntFE0Wi4YqyaVC30OUC83TPofprZ2NKDz4PinJ/sfpv5di1QODDhCvVMdNgOOQrmx7Hsw4Ei1MsMmlQt9D1If5fOc+OuJ9n3F6NdB/8P0yA2LfofoOXNaF0HHAerlhkn/w9QHVeyUnBNxb7oy4AjwFDsalg17HaVWZthYlsxdCHdbwtn1lgWjzg3lU9ooOUvPQ2ZuBkrOVi83TEafpz5iAOLcjDhdvdywSOfJ+FX3lge529D3IPVyw6JDP+gxTr2xbKeh38Hx7ioy8Gg90XE7LWddnB3ckrPUG3wgMuOuJ0adq+cBPc+BkWeplxsWlgWjz9VT+ZDXEgYdq15uWLTsLE6a6rstti334Do2/SK1NnofAK3D0XMhORq2RAx6jA/lc8rJaQEjz9Bj8KVyYPBx8W3B12mg2s4/NbFsGHOh2keMwmTQsXqyaCAO7pgL9MgOg1Hn6TH4QBzcUTE2+sZcoG/uWnaOb1bDTktQRYfBFzi4bTRlU5pK62Lof6h6gw+qfm/n6tHvYdD3EH2BDc8RHRtXdDqYniPnRVwZfX5owffwQktuBiZcFdrnlDLqbMjV+IiZnYaxF+uT3xQmXKknSgXiYLTvE99Slv2uVd/qMSBwcNsqrpkOg9yWMO5b+u42WTbsc3k8HwPrPFhKWHTNnefAxO/qkd1Uhk5W3+WsJr4H+1ymT35T2PsylF6gr01hJxh2sj75TWHf7+rTsXYa+uwfzwYYqRyYcIW+DKqdhrEXQZ6mYGJTaNMDhp4YmnMe3m8glQODj41fLWQqF/a9Ru8Y7JRsiNyWesfRUFoXwcgz9USpAjwH9v++PvmNpe9BVRf8NHZr8z3Y92p98hvL2AshV2OLWcuCFh3iWcqy77V6SkQD7DT03heKx+gbQ2OwbNj/e/oCA1BtuMQt+13QDsZdrLfpiefCft+LX3fMbiXQ90C9OtZ1YL/r9MlvLCNOlwyqzoqHnAJZ+3Fj4pWEGRgI19XzPDjhtniVshz4QzGYddcN57WGw3+pdwwN5bi/o6VzTU3stNTqx6kMKKdA5k5X+UpAYLjEqStL215w8E/Qvu58Dw7/ld4Id0PpezCM1HQnqCauAyfcqn8cDWHCFdBpkH5DNZULx/xZ7xgaytF/1H+h2E5Bx/7xqrqw03DirfqyGQGpNAw7KV7NCAo7wRG/VX8J/BtYcOCPoF0fzeNoAMVjYPyloQYGwrWug0eZ9o5JerdolJSv6HYyQA7Ccd+KTweqkrOk1lpnpCXA9+GoG+NTv3zoz6BN92i0NfY9OOmf8Xgp3LLgxNvBztEfzLBsudt1/N/1jqO+5LWCybfrjcgHpNJyt+uAH+geSf3oOED2rO41B3JmDDtZyh7jwMBjYMRp0TjrLBsOuUHWXhzY/zroPCQaOtZz4cTb4tNi+dibpUJEeUvgWliW2HaTb4/G+bEn0nliD4Tc3Cf834JlwWG/lDaAUSaVC5PvQmvdaG08FybfEf0Sqlbd4Og/6ek0tSssS4zPE27VPZI903OCOOK6I6MBdhra9hQFHHXGXixlN1FQvCDj2OuoeHQ+O+I3UNg5OuvOsqXksdtI3SPZPZYNk+8kG6qy0XieZESjXkJV0A5OuCUazu3/sETvR2Uf1EWXYVJtEYUgKMh8tegAR/5O90j2zLCTpYw/MnoiR/T++Et1j2TPHPRjyb6EHBjIzioODucob+aDfyyp1ChEWgLsFLTsKkZBVLEsUR7p/OgcgiCbue+B0b5Un1sIJ90VHQctILgj1HMf3SOpm3Z9ZF/oeK9ld/geTPqLlF9Glf6HSmlh1M5j35f9oLusZndMvFreWIqK0QISpc1vDZP+qnsku+eYP0sEPErrLpWGbiP038vcHancKj2heyC1sNMw6hx5WyGqtOoKx/41AiVTtfB9OPzX0pY9qnQfJ3c2s5AFyo6lGLzaHNX7GqPPi07JVG3slFx2nRjRWtKj/gj9NLUp3BO+D8f8MZrtM9N5cOYj0uYxSs5tgO/BmY9Gs+93y85w3tMyb1E7Tywb0gVw7lSJ4EaNbiPhtPsjFlWuIpWGDgPg1HuiuSeGnAiH/Tx6aw5kvoaeCIf8VPdIds1B18PwU6L5ew1KqKLYhcpOwSl3y32gKOpYz5X9WhTBF8Pz28o5nFOov2SqNpYl6+7cp6N5r69Dfzj78awFQbP327BsKSk4+k/ROqiHnQzH/S16kdHaHPEbuagbJQ75Kex9abR+nzWxLMCG0x+E3vvrHk01qRxRHr32jVZ0ryZ2Wkr2LnguWhfXWrSH854RBy2ql4dTaYlUnfNUtFoZdhooDlo6L8LrLiXlZyfeHq0xDjgCTv637lHsmQO+L0GzKDHxKjjoR7pHsWdOuitarzdbNhx/Kww6Jlp7oSZ2Ss6Tc6fK/ZGokNcKznlS7lNF0UED0V+tusH5z0FhR92jqaZtL9H7ua2yFhiwfD/LFrfvQ+mD8OzV+qNqo88TJwOimc2oie+L4fzyT2DaLXrHYllSm7nP5XrHUV88Vzo6PXoOfPaK3rHkFIjj0/fg6EVZdoXrQPkmuPdYWLtQ71hadZMDsG3v6CqPmngOrF0E9x0PO9brHUvRKDEGclvGY+58DxY9D09cBG6l3rEMnQwn/wuw47FnAd7+I7z5W92jEAfjoOt1j6J++J7889SlMO9JvWNJ5cBJ/4Ihx0ffNgE56yp3wP0nwsoZesfSor1kCroMiWYGrTauA5uXwT3HwpaVesfSaaA4PgXtshrIy76jAbKZP38Dnr4ctq3JurhvkMqVOxn7XVttwMeJj/4Jr/4MMjvUyy5oJ87Z4OPjNW+eB3gybx/crudeRId+EhXtOiK6Eapd4Tqy1p6+HBY+q2cMPSfAKf+Gwi7xMJQDXAe2rIDHL4SymXrGMPxUuSyczo2H4g3wXFj+EUy5FDYtUy/fTomOOPgn8v/HweCryexH4IUfQMUW9bLzWsFRf5DHb+OE7wM+vHUjvHOTnpbjbbrLndaeE+Pj2ILMlZuB566RtaeDolFSLdCmR/z0xI6v4ImLYel7esYwaJJkknMKsz53ahwNqDZenr8O5j6uRCQgF/lOukuMvrgpjgDPFc93yrdh6fvq5A46Fo7/h1w8jJPBUhPfhxUfw9TLYP0XamRatnSWOuwXYKXidQAGeJ4ovXlPwvPfg50b1cjNKZBWontfJs5hHNed58gaePdmMWBURehbdpagwMBjqn9/ccN1wKuEl34MM+5WJ7fzYOlG1HVYjPWEA9vXwdTL4YvX1cntd4i0Po1SV7OG4nuwZr5kN9YuUCd39PnSmj2VF0894XuyXz59WapWtq5WIzeVK1259rsW8OOrJ+w0fHgnvPYLdYHkgnZypWHEqWJbKtiz6hwNqFZ+n7wAz34Xtq3NnqyaC9H347mJaxIYLx/eCa//KruL8msLMaYGS01cB3xXNvOHd2Q3u9G+r7Qo7rF39mSoxHOgfAs8cxUsei67snruI8Zem+7xNVhq4nvi3D51afazG8NPgUk3KYlOZZ0g67z4bXj6Cti8PHuy7JR0IDroesCK/9wFhsPM+6TsNpvZjbxWcpdwzAXKDJasEjyM9/aNEiTIZnajdbF0b+x3SDyrLGrjOuDslKDUnEezK6tbiQSPOw6Ib1CgJp4LW8okk5vtQPLAY2Td5bdR6pypdTQCXAfcCikJ+vAO2LoqvG/ntJD07b7XROPF77DxXNixDt7/B8y4N1xFUtgJxl8i0eTcwnhGCXaH78O6T+Ddv0qk3s2E9+0O/WCfK2DUubLm4m6w1CRwNpe8A+/9FT4POVrafRzs+11J5cY1i1EXrlOVGZoC7/8dVpWG923Lksus+10HPcYnIyhQEzcj592Mu6X8cdPS8L6dzpNmJfteA+37JFBPVAUIPrgVPv53uBnJgnbSqGTCFdLpJ+4ORm18DzYuhfdulpIgpzy8b7ftCft8B8ZcKOdcVBtcNIYgu7FiuujYT14IN6jXbaS87D7spGQEj2sSZDcWPQ/v/Q2Wfxju9/sdKjq274FaggJ6HI2AIGLwyYsw6wH4/LXGRxGKRstr1SPPECMZkqc8AoK60ky5lKGVPtj4hWnZ0PcgKDkHhhwnpT5JUxw1CTbZ9nWy5mY/BF990rhvpfPFOB51btUG9pJ1+NXGdeTn27BYnNw5jzY+SFDQTjrAjT5f+tq7mWQp3doEP9+K6TDrfpj3VOODBG16wMjTZe7a9qxWUkklcNY+ew1KHxB90dhytC5DRU+UnC1GMn5y9QRUN8aYP0X0xJfvNK7jomVJ17ySs2HoSbKWk6wnfA+woHyz6IjSh2D13MZ9K5ULex0pb1D0P5zYlvrUl+A82rwCZt4rzlpj71zltZLmDKPOgx7jmoGeqNKxq+fK3M19ovFBglZdJZgy+nwJhAbf1oBeRyMgmACnAlbPg7IZUFYq0b8NiyWiEAwznSeP2hWVSAqteLRcCMpvk/xFuCuCn7liq8xZ2czquduyUuYURFGk8qBdL5m3ohIoHgtdh0tdfHOcu+BA3L5OOmcEc7d6jvy7wJixbJmjjnt9fe6CXudJN/Rq4/tVmYeUKJMVH1evudVzRTkHAQM7JW3zOg+u3rPdx0kUGeRRqiRF4feE58p68n3Y8HmNuZstXb4qt1VHAe00FLSFriNrzN1YydQG34l7yUVDCPaZWylztWK6rLmyUlj/uZRuBHoilSt3VrqNrNqzo0VXtGjfPM+64Geu3C5rbeUM+c9VpbBp+dej9ul8KV/8n44dI/OYW9hM567KPtm5EVbO/LqO3ba2hp6w5E2dDv2+rie6DJH1qNHQ04LvS8mynZbSoJr7dfUc6W4YVBVYtnTI6zyoeu66j4P2VXdrA33TXPA8CI72jV9+XceuWQCVW6u7uNppuUfbZfjXdWzbHlV6wtIeTImGo1EbN/PNx7l2dcC5GVl8SY5INQTfF2W8q3n6xr+rBDuneRkqu8Pz5FCsOU++Jxt1T/+uueM5gPV1ReC5fCNy57nJS3k3lV2dYZ7DN1qr7mqOmzt16Yna8+lmqjK1Rk8AVXoiI8ZvTerUsRF8KFMX9dUJdc1xc2aXOqGqO2Ttf1dbFzd3XEf24Dd0LLX+XRDgi5aOjaajYTAYDAaDwWAwGGKNCfEYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAgd42gYDAaDwWAwGAyG0DGOhsFgMBgMBoPBYAidtO4B7BbfB8+R/wywACsFdkrbsGKB54HvgI/MWfCfdhos41/uFs8B35M5A5k3rKq5szQOLAa4DuDVWncWpHL0jisOuBk562ruV8uWdWeoG6MnGo3n+bi+//WpsyBlWdi2Oet2i+eC71brCZDJM3piz9TUsUZPNIxd6QlsSEVXT0RnZIGyCBZa+RZYVQrrv4DMDplcOwU5BdCyK3QfC626yp/1HMQQbKZKxfNk0wYLbdtXsHI6bFkJTrnMXSpX5q5tbygeDQVt5c8G89pcnY/aa2fTMlg5A7avk7nzXEjnQk4hdBoE3YZDTgv5s26meR+MrgO2LWvHc2H951A2C8o3g1MB+JDOg9yW0HUEdB4s81V7rzdH3Ey1QeJUwNoFsGYeVGwDtxKwZO4K2sl+bddX5tr3ZL9HWKlknZr7rnI7rJoN6z4VPeFUyl5O50PLzlA8FtoUy59t5nrC8308zyedkrN+445K5izfxPKNOynPuGRcj5yUTX5Oiu7tChjZvS3tCnMBcFwP27awm6sB7bmAX+30bymDFdNh25paeqIFdOgP3Uogr6X82eauJ2ruO8+DjYtFT+zcCJlywBf7JLcldB0GnYfI2Wf0xNf1hJuBrxbJeVe5rUrHImddfmvoNgo6DpB59j1ZkxGZO/3aKtiEm5bB/ClQNhPKSmHT0j3/3RYdoKhEJnivI6HHeFmYzSUCGPysq+fAJ8/LvK0qhW1r9/x323SXw7BoFAw5QRZoczkQfV82omXBkv/CF2/I3K2eI0by7rCsKkUyEorHwLBTxKBxneZh/HlV8+ZWwqIXYOm7suZWzxOFuztSOdBpsOzZHuNh6EmQW9h89mzwc5ZvgflPwfKPZO6++qRKGe+GnBbQdbjMXe/9Ya+jqhQK4oAkneBs2rIK5j0hhkrZLNi45OuZjF1R0E72a7cS6H8Y9NlflHAzcTgc1yOdsvlszTZemr+auSs2MXflZtZsqdjj3+3SOo/hxW0YXtyGo4Z1ZWDX1v/7XrMgWCfLP4TPXqnWsTs37v7vWRa06yNrrng0DDsZWhc1Ix3ryX96Hnz6Enz53yodO1cCArvDTlUF9UZC9/Ew7CTIb9OMdGzVmqvcLjZxoCfWLpD1szvS+dBlqNh1PSfC4GPFifN9rXrC8v09ndJZIvBy5z0F0/8Fyz5o+jfb94XR58O4b0FuC/l+EiMwngduBcy4G2bcK15uUykeDWMvhhGnV5VrJFAJe55stu3r4MPbYdaDsHVV075p2dDvYBh/qRh/ST0MAyN5/Rcw7RaY9+SenbI9kc6HwcfBhCvkYEzq3AXGxbIP4MM7YNHzVVmLJlDQDkacBhOuhLY9k2s4e47ssUXPw0f/FIOlqSqrTQ8YdQ7s/W0xYCCRGV3X83E9n8enL+ehj5Yxv2xLk785pFtrzhzfk9PGdSdt26SSWF4VrK/yzfDRXTDrgfoFPneHZUGv/URPDD5WDPEkBleCc2jTcvjgVpjzKOzY0LRvpnJh4NGw92XQa2JynbVA/5WVytwtfBYyO5v2zbzW4uROvFICpJqCeuodjWAhTv8PvPMX2LwifBm5LWHshXDwT2RSk7Iog/r3t/8oSrd8U/gyWnaBfa+GfS5P1mHoOVCxFV79Gcx+pOmG3q7ouBcccoNkiHwvOcaL54qifelHEtXLxpHRcx844reSJUpKcMD35Wf58j149adSkhc2lg2DjoWjboRW3ZLjbAR6Yvaj8PbvYcPi8GWk88XhOOwXkC5IjJPruB6WZXHn21/wr3eXsGF7+GdduxY5XLxfX75zUD98309OhsN1JDP7+i9h1v1NN/R2RbvecOAPoeSsZAUIPBe2rYaXfgwLn6nOaoRJ0Wg4/FeSlQzO1yTge1IS9fKPYen74X/fsqD/4aIn2vVRvubUOhqeKxHkKZfBl+9kX17bXnDibdB7v/gvSt+XUoEpl8K6z7Ivr/tYmHxX1aKMsRIJDP75U+H562DH+uzLHHICHPc3yGsVb0ctiCa//w9483d7Lo1qKpYt2Y1Df0bUL7ftETcj8/fKDTD939lxzmqSWygG8/hL42+8eI6Upky9XBzbbNOqKxz3D9jriNgHCDzf5/O127jmkVIWrGp6BmNPDO7Wir+eXsJeXVrF+/5G8Hv//DV4+sqmZ7rrQ//DxD5p0SHmeqLqvPn4X/Dqz+X+QLYZexEc+bv4B5KD4PEbv5FKAc/Nrrx0Hhx0Pez7XaWBZDWORrAQP/onvPZzqT1ThWVJSdARv4nnogwW4uu/loWYjShBXaTz4eAfw8Sr4pndCLIYz14DC6aqld2iA0y6CYaeGE8nN8hiPHUprPhYreyOA2DynRK9itu8BXz5Hjx9OWz8Uq3c3vvD5Dvimd0IShtLH5bsWTYytrtj5JlwzJ/l3IuZk+t4HhYWt7zxGbe8+TkZV138MG1bXHFwf64+dEA8sxtuRi7Wvvh/UPqgWtn5bcRgHnVO9fqPE0EWY8p3YMnbamW37Qkn3CbZjTji+3L3Ysq35Z6eSorHwEn/VBZIzr6j4bmykR89W6IFumjXB857GloXx0eJuA7sWAf3nwhrF+obR6+JcNbjkJMfH2fDc+Ti2YOnyJ0MXZScDcf/A+m6ERMl4ntyCW3q5dnPYtSFZUuEft/v6pHfFN78Lfz3T9nPYtRFbiGc/G9pkBGX6Lznynw9dYlclNdF6yI456mq7i3xOOsc12NbhcP5//mI2SuaeG+qCQwvbsN9F42nVX46Ps6G58i9s/snS5dGXQw+Hk75T7zuR/oefPYaPHGhmizGrrAs2O97cOhP9chvCtNukVLubGcx6iKdB8ffAsNPzXpAL7uOhudI+7IHJsvNed207AznPQMdBkTf2XAdOfjumQSbl+sejXS8Oe8ZuVwU9bnzXFg2DR46Xd8BWJNBk+DUe+PTRnj6f+D576nNntXFfteKwxEXXvi+ZG51Y6cl2jfi1OivuSAY9ciZ0gFON/lt4dynpOtNxJ0Nx/XYuCPDGXdN44uvFFYK1EGfjoU8cuk+dCjMjb6zEQSj7p+85y5SKuhzIJz1qFx+jrqz4fsSEHjq0j13zFPB2Ith0p+jf9YFvPFr+O+fdY9CHIyj/wTjL8mumKw5Gp4rnZHumQQrZ2ZFRKNo0R4uflXek4iqwew6ko7812FqakXrS+fBcNEr0tErqgrYc6TU5/7J2bnI11gGHg2nPygHYVTLgXwPZt4Hz0Ysi7DfdXDYz3WPYs88/z2pU44Kli3p8WEnRVcBe548evbAyepLL3ZHXiu44HlpFRnRs85xPbaUO0y+7T2Wrt9Dy1CF9GhfwJTL96VtQU50nQ3Xga8Wwt3HQEX277LUm977wblTov2wrufBwqfhiYuiEYwKGHsRHPtX3aPYM1FxMmoy6SZpoJSlNZe9lez7ElGOkpMB0mrtnmNh+5poeOK18Vw5+O45NlpOBkj51v0nyCGtqyxkd7gOrF0ED54aLScD4JMXpRYzqk6G58KCZ+C5a3WP5Ju8exO897dorrmAN34dLScDxAiY8m34/PVonnUgr9o+dl60nAyQu133nwgbl0Zy7jzfp8LxOOufH0TKyQBYvmEnZ971ATszLp4XwT3rOlIlcN8J0XIyAL58Fx49R/co6sZzYMlbUuIYJScDJBP/2i91j6JufF8aq0TNyQB44Xswb0rWyriy42j4Prx1ozyGFkW2roInLo5mxMBOiXGwcYnukeyalTPhlZ9E02D2XXj8PDESosjcx2HGPfpqMusiuND39BXRUx4Br/1CHvN0I2b0uRlY/HY0lQeIYfDUJfImQBTX3bRb4ZMXdI9k1+zYAI+fr3sUu8S2LG6YOo9Fq6N51n22dhs/mTIPO4rvbFiW/F5VdCBsDJ++DO/9XTIHUcLz5LHRJy/e88Nxunj3Jim/jJyecOTi92sRzcz7Pjx7ldjGWQishG9pu1V1j+9FPIW1bJo8nhUl5es5UPqQmpaOTWH6v6WrTtQOm9d+IRf7oswrN8C2NdFad3ZKWk5H4T5LXQTReSKkfH1f9sDTV+geye7ZuVFadkap7ttzpKvZG7/RPZLds3quvFsUIQfccT1eX7iGKbM0Xl6uB8/MLuOV+atx3OjMHb4H7/xZ3iyIMm/9DjYujlY2zbbh2aub/gBftnn6Sinbj1QG3Bf9FSW9X5vK7TLGLJSKZiGk78tbD1Ge0IDXfyUPBkZhM3uubOCXfqR7JHvG92Hqd2TeorCZXUeaDXx4h+6R7JmKrdLNKSpGn+dKyU9Us481WfeZ7NkorDmQyOjL10ejWcOe+OQFmPNYNM46kGzyU5fq62rWEN75i5SNRiBK6nk+OzMuP3pqru6h1IsfT5nL9sqIlFC5jrQR/e+fdI9kzzgVkomMStWF58C8J+W16qizZSW8+MPoVF34Ve9kqG5h2xi+fBc+vDN0+z3cVRyUTOlsxdoQMjvFKYrCZrZTEhkt19eesEFsWirR+ShsZt8VTzxCUcfdsvhNmH63fmfcc2HrammxFxem3SovbOs2+oKSqRn36B1HQ3jx/2DnJv37JCiZUv0+S2PxHHGKInDU2bbFT6fO56utFbqHUi/Wbavkhilzo1FCZSG/x6hl4uti5Uy5m6a7hMr3pWTqhe/rHUdDmHW/3E3TriccyZ5N+4fecTSE134BW8pCtU/Cs7B9X2oe34/RhAIs+wAWPa/38PEc8SSjXjJVm+n/qbosqfEg9Bz4+J+wYbG+MTSGN38jDpJO7BS88Su1D2g2Fd8TB1d3x7hUjtxVihM7N8Lbv9c9CslivP0H3aNoGGvmSVmrRsPF9XwWrd7C1NJol0zV5tk5q1hQthlXZ1bDzcDsR2H1HH1jaAz//TNkdJ/PPvz3j9EvmarNKz+JgJ5Ii77SHVRsCJkd8PovQ626CNHR8CRK5VaG9kllvPc3vS+G22l472Z98huL78ncac1qWPBBDEqmarN9Hcx6UJ/h4vuwba2kw+PGsmlQNktfGZDrSDZjdTzKV77GrAehQuNdHM+Bj/8d3YYNu2PaLVoNl5RtcftbEb+DVge3v/UFKZ1ZjVROvKLKAZXb5F0enSWPlTtg5v365DeWtQvhc40Xwz0XVs2RIHLcmD9Fqh1CKlMO19GYcXdon1PKio9lQeiIzPs+bFqm99X0pjD7IfGAdeA6Unsehxr5XfHRnfoMF9+Dj+6KTxlBbabdqu99g1QaPrxdj+ymktlR1flMk/K105KBjCNffSIOpqa527i9kufnRKzleT15Yd5q1m/TVO4VVAzEpaS7Nh//U195t+vAzHuj3Shkd3x4mz4da6fgg9v0yG4qniP2QUhltuGs3sDgi8Lrmo1l5r16anB9D2bcG50Lrg0lsxPmPqHHYE2lpRYzrqxdKPWbOhxcy4LZD6uXGxaLntNX8rVjPXz2qh7ZYVD6gB4nzXNhyTsSWIkrs+7TMneO6/HEzBU4UbhU3Qhcz+fx6Sv0dKCy0/IQaVzZUqbPwU2lofRB9XLD4os3YPtXemRndsCCp/XIDoPZD4dWrRKOo5FKw5xHQ/mUNnSVkNgpmPuYHtlhMedRPaVn5ZvjmwkKKH1IvYPre7DsQ+m4FlcyO2H+U+odXDcTre5NjeGrT+TOgWrsFMx5RL3cMFn4HGTUd8pKp2ymRryd7Z6YMmulnpfCnYp4dEvaHbMf1hMc+GoRrJmvXm5YeC7MfkSPnljwrL5qjzDYUibPGIQQCA1v1y//MLRPaWHnRtig4ZG87eviHeEDeUhNdVTe92DF9HhdstoVKz5Snxb3XLnnEHeWf6xe+aZypJVy3Fk6Tc99urjrCadcnDTFGejyjMvCVRF7xbqBfLp2KzsqNTjoaxfE2+ADPR3a3AwsfV+93LBZ8bH6QKidiv9ZB7D8g1Ca1oRj4WxbIwZz3FnxsdqLQ54nrTrjTmYnbPhcrUzPFQcn7qyZrz46nsqRV0rjzqpSPY0IkjJ3tmLlm9kB6xWfE9lg5XTw1EZIF67aQkyrpv6H78P8si34Kp00t1ICUnFnw2L1TRzsNJSVqpWZDXT8DJYNq2aplxs2ZaWhOGlNdzQ8NxkbGdQbLr4r3XOSwIqP1aYnUznJOASdCnmITjVJmLu1C9SnxCu2xa+V8q4oK1V81vnScCOud9FqUlYKqVxl4jKuR+nyTcrkZZM5KzapvWdiJySoAvJzqNw/lpWMudu0VN4BUYnnxLvkLCCk33/THQ3fT46xXFaq9sXmpESWocpwUfzadVLmbsV0tQZz+RY5fOOOm5EaYlX4fnLW3FeL1JZOeZlkZG9B+RrISdnMWxmTh1z3wNwVW8hReU/DspIRVAF5wE9l9tvNSDAnCZTNUuukrftMgohxZ/MKeeS1iTR9x6fS8W0bVxsdP0eS5s5WqEAqd8T7MnNN1i5Qe08jKWsO5AEuVU6al9FziTobeA6sV/gmQyo3Oetu3afK74Z9siaG747sgk91/BwqgxHZZO0CtXcNNn4Z3/bntVkzT125o+fE72HI3RFCZiYc6yauPZZrU6nhEIzjw1W7QvUa0P5aaohUblObSStPRnQUqKpbVhip0vnYXdioPnuSoic8F1y10crtFTFvelHFtgrF99Gcinh3iKuJ6v1TEe/mA19D5dz5XsL0xOYmZ4PCadniqG/3lxV0dDBKQnoN1P8cSZk3kBIW31dTM+97ydmvIFEqZSlxK1lzp7rrVKLmzgGFweWKTPZ10yljunPa2B5ZlZGbVty8QUdntWyhUk9AwnRsBqV95JN21vlek0rjQ+oNqeOluyzheWpLgHR0zUkECZo3lcoDkrXmfB91a0Hx7ynbhPTqa/1J0NypzKIBloJ1t3lnhmUbspspzs9JUdKjXVZlJBbVeiJJqLyf4ZOs35Pv0dSzOxxHI50fyme0Y6fVOhkA6bxklE+pXgPpPLXysonKubPs5OxXkHWg0M9I1twVKJaXoLlT2HUKIC+dfb306oI1vLpgTVZl9OrQgmNHFGVVxtdQ/HvKKsp1bIL2azoPZcEBy0rY3OU32S4O5/TKbxPKZ7Sj4+cwc9c4clsmJ2qQ30Zt2V5Be3Wysk1+G8J8d3S3WCRnvwIUtFUny/eTM3epXOWBjtYFit88yRKt8xX/HOm85ASlVO8fledDtlE5d5adnLMOoEXT7YWma2g3A12HNfkzkaDrcPUyu2iQmQ26DlNrLOfkQ7s+6uRlk67D1KZ2uwxOjpNWNEo636kglQvdStTIyjbpPGivcP94CdITnYco7RLn+T6Du7VSJi+bDO7WWu2DfQBdhqqVly26DlN756Rtb8hRnPXMFt1K1D1QaqdELyUBywpl/zT9tLRTUDS6yZ+JBN1K1PepLipRJy+bdCtBdd1yYjZz93HqjGWAnBbQob86edkipwDaK/45ugxT24o4W3QZKqWiqkjlQvFYdfKySVGJ0sCA6/kML05GhHR4cRu1D/b5HnRLiJ4oHqu2FMy25byLO5YF3YarDa616y1VF3GnXR/ILWzyZ5quMS0bihPiaKg2+pPk+XYfp9ZwcTPJiC7ntpRDSTVJmLsuw9TfqcrJh457qZWZDbqNUn8ZvMsQtWdEtlAckMpJ2Ym5QD2yRxu1D/Z5bjKCeZYGo9/zkjF3HfpLcE0llq2nQiZsQrJPw9nxBe2gdXEon9KKamPZspORDcprDe16qZVpp5Ph4HYdrj5C7lYmQ4EUlYgy1CE37hSVqG/nncqFToPUyswG3ceqfTgNGNClJTmpeJc7pmyLQV1bKxaak4xMWse9JMihEt9NRkBKx8+QFAe3W0kojzaGZ+H0mhDap7TQqhu06a5ebn7r+Cvfnnurl2lZ4mjEvbtDr4nqDT47B3rvr1ZmNui5r/qovJuBXvuqlZkNeu+v3FjG9+KvJ/LbQKfBysXmpGxGdm+rXG6YjChuQ66C7lnfoNNeEgyNM70mqr3HB3I+9DlArcxs0Gui+hfOfT8hemK/UILv4ex6z4GRZ4fyKW2MOF3Pg32eAyPPUC83TEacqX4jg6RDB01SLzdMSs5Wn9GwLOg2Mt4lQPltYPAktXdbQJTvsJPj7eAWj1Z7ETzA96HkHPVyw2ToZCl5VYzjepw0Ot5VAyeN7o7jashAWjYMPUm93DApOVvDuzdA257QY7x6uWGRzoNhp6gPqqTSsNdR8XZwO/QTXRHC3ZZwLBw7Df0OgtYK+2OHzZjz9VzytNMw6pz41i7nt4Uhx6nfyCBO2ujz1MsNi+7jZDPr6ADlObLu4sqwU9R1EalNbiEMPk6P7DAYda6ewICdknKCOGdwR5+vxeBLp2xOLCmmIEe9kxMGeWmbyaOKSau8nxHgI/o9rnToL+V6Ghxc3IycF3Fl4CSpGtGBnYLhp+iRHQYl54R2Fy28Xe97MP7S0D6nlP6HQvu++lp+FnaCISfokd1Uxl7YpKfpm4Sdhr4Hxddw2edyPQYfyNyNuTCUjhLKsSyYcAXKu5wFeC5MuFKP7KZS0A5GnqUnMADgOrDPd/TIbirdx0qET4fBBxTkpjh5jIby3hA4eXR3CvN06QlbMrhxjczv8x3ZNzpI5Ui1R4sOeuQ3lQlX6KlU+Z/8K+PZSj6nAMZdHFoAPDxHw07DuEsgL4b9vve9Vt9GBtkI+1+nT35jSeXKRtbZ7tPNwMSr9MlvLG17wdAT9Rl8AHkt45nVGHBkVSZI07oLIvNxrMEdd7HeNZdKQ8lZ0LKzvjE0lonf1aonfB8uO7AvdszsFtuCyw7qpyssILgZ2PcanSNoHC06SEZBdYloTew0jL9En/zG0mNvfZkgEP3UrjcMPEaP/KZQcrY0+QmJcDV1TgEc/JNQP5l1Bh0LffbXvJFT0roubkbf/t+Tg1Cnx57KEcMlbh2ojv6Dno5JtTnox5JRiwvpfDjq92rfu9kVrgNH/zFeJY9tusN+1+lTvAGWDYf/Ru8YGkqfA2DI8Vr1hG1bFLct4KL94vVQ6QUTe9OzfQts3Xpi0CTJgMeJw3+tf7/aKXHS2iruLNkU7BQc8ye9AWQQPXXkjfF6+LBFBzjkhlCbD4TraNgpSfP1mhjqZ7NGi/Zw/D+iYfD5vhgucWkT3HUEHPD9aDxe5nlw0j/l4lccGHEaDDxar3ML8rvLawnH/lXvOBrCITfIBUXdBn4qLe9CxCkTecKtkIrAHrHTMPJ02QNxILcQTrxdbwlGFZZl8X9HDqJvx3iUPPbu0IIfHh2R0lbPld9jXKouBhwBo87Wf9aB3Ic78bb4lAHte420jtetY+20BHgO+anecTSESTfJHgnxjarwrUTPgcl3qX8gpTEc82e5KKT60a9dYVliBJxwi+6R7JlUDpx0l7YS+W+QSssLlgddr3ske6ZlFzjmL3o6iOwKOy0Xm4edrHske6bHeCnV0x3hC7BsOPCH8Xg9d8wFEs3VrXgDPBeOvyUeXVkO/5W0P4/IurMtuOn0kZEvobItuOm0ElJRMU7tlJTsHf5r3SPZM/ltJTAQAecWkHOj934w9mLdI9kznQdLpj4KQVCoDsD3jEFr7yEnSkl3yM5t+L8JOy3dpw77eeifDpXAuIpCtCAglYZ+h0S/hOqAH0CngdExWqAqvftdKB6jeyS75/i/ixMelUMQJCM06aZol1Cl8yWAERUHLcBHnO4onSO1adMDjrpRfR/+3WGnoKAtHPUH3SPZPX0OgHHfioyTAdKBamT3tly8X1/dQ9ktF0zszehe7fR0mqoLOy0NTPoerHsku+eoG6XiIkLrDt+HI34T7RIqOy16Imr4Hky+M9olVIUd4bibs6Jjs3MC2CnY+zIpEYkinQfDCbdFo2SqNr4vmZbuEX3NdOAxsH9ESqZq43tw+v3RbbO833XSWztKDhpIRi+vJZz+QDQPQsuWtH0USqZqk0rLeXLsTbpHsmvyWsEZD4GdG72yh6CEKqoXTdv1gVPujk5UuQaWZfF/Rw1k3/7R7AY0oW8Hrj9G/cOG9cJz4ZT/SKfJKDL2Irl3GLWzzrJkTGc8FOpF4VA55k/RKJmqTVBCdeId0bSd0vlw2v2iL7Iwvuz9xL4vkxq1Otx2feD85yC3RTRKpmpjWVKadM4U6DxE92i+Tp8D4LR7o2ewBNhpKOwsv9+oteMb961oZ/nstLzrcfoDersS7YpJN2l7KK1eWLa8rxC1kox0Ppz1mNwliZrirckxf47eo6WtusEFz0kJS0TXnW1Z/Pv8cYzq0Vb3UL7GyO5t+M8F4/Re/t4ddkoM5Quei15QavgpMOkvukdRN6kc6DwIznkiekGpQ38uTlqU192Q4+G4v0VrjHYaTrtPunRlybnNnqVtWYAFpz0QnTciOg2Ei1+RV4WjFi2oiZ0WR+jCF6FolO7RCP0PhbOfkDczouiRB6RyJLV70ctiLESBvS+LtvIIsFPQ9xA485FoKBHLhuP+XvVWS4TXXMC+V8ORv9U9CiGvFZw7JavKIzR8Xy7pRuVhsLa9RE+07BJpBy1lW+SkbB68ZG/G92mvezgAjOvdjocu2YfctEUqypdIUmko7AIXvyotSKNAyVnS1IQIzxvIeVI8Fs6dGp3MxuG/jkdjDsuWR4aPvyUaAYx0vmSoBhye1fFYvp/lwt2g3uuFH8DH/8qqqN3Sc4IYULktI608vobngFMBj50Ln7+ubxwjz4Djb61KnUZgc9QHNwPb1sCDp8DahXrGYKfkUtoB39cjv7F4Lqz4WNbdtrV6xpDbUi5DDjk+Hk5GTUofguevg8xOPfJbF8OZD0OXodF3MgJ8X86X138F796k7z5JtxI4+3G5pB61zF4duJ6P6/l895FZvDhvtbZxHDm0C38/cxRp2yIVxWqBXeFmoHwTPHgalM3UMwbLgolXS9OBYB/EAc+BtYvg4dNh8wo9Y0jnS0Z0dESCFPXF92DRCzD1MqjYqmcMhZ1qZDKya9dl39GA6s3zxRvw9BWwpSzrIv9HOl9aYk64Qn65cVG8AZ4ri2D63fDKDVC5TZ3swk5w7M0w+FiZu7gZfK4D+PDm7+D9v6mtte40UC6ldRsRv3kDUSKV2+G5a2Hek2pl9zkQJt8OLbvGx7GtiefC5uXw1KWw/EO1skedI22yU3nxCajUZtkHMPU7sGGxOpmpHGlysf/3AT92esL1fFK2xdRZK/n5M/PZvDOjTHbrgjS/PG4ok0d3x/N87ChnMnaF64h98u5N8PYfwa1UJ7tdb8nmxeVJgNq4DrgV8NL1MPNetbK7j5MMUNue8dUT29fClO/A4jfVyh46WWy7vJZKzjo1jkZAsChf/D+Y9UD25fUYL8ZeXBdiTTxXIvRTvwOL38q+vKEnyfsKihZiVvE9WDUHplwKX32SXVl2CiZcJc4tVnyNPZBmCbYNC58Vh2P7V9mVl9tSonrjLq52sOOK54iDOe1WeOM34JRnV17rInkTqP9h8YqK7grXAd+FV38KH92V/exG1xHSOazTwHgGBWrgeB6bd2T44ZNzeG1h9rORhwzqzB9PGUHbFjmk45LFqAvfg3WfwVOXwKrZ2ZVlWdIq9ojfyBsVcdYTXwskXwlbVmZXXjpfHoaeeGU8g8c1qRlIfvWn2c9uFHaUO49DTlAaPFbraED1olz8lkSasxHxa9cbJl4lF4PivhBrEizK0ock+rLus/BlFI2StwEGHl1taCaBILvx/t/hwzuyUxLU/zB5mCeuWYy68Byo2AZv3Qiz7pdMR5ikcmDYKTJ3rWKaxaiLILvx+q9gwdPhv2ie11reyDjwh6KA42yw1CTQE8s/hDd+C0veDl9G6yLY53Lpce+TmLkLshvPzynjljc/Z+Gq8I2XgV1aceUh/ThuZPH/5CUCzwEs0RHTbs2O0dznACmp7TUh/kGBmgSB5Lf/CDPuhvLN4X7fTsHgE+DQn8o9qqTpiW1rJCg19/Hws2q5hVByNhz846rH+NSedeodjQDXkYN95UyJWi18pmkGjGVLb+wx58sbGUlyMGoTzN2nL8P0/8DnrzXNgEnnw6BJ0hmp10SpW41JfXKD8Rw53Oc8CjPva7qjW9BO3mPZ+zLoOEC+n8R1F9y1qtwu0ZfSB5qeHWrTA0rOhHGXyENacc9i1EXwc21ZBR/dCbMfga2rmvbNLsPk1eDRF0BOPmAlx2CpSXDWrZkvemLeU1CxpfHfsyzota90CRt2kvy7JO5XwHE90imb9z5fxwMfLOX1hWupdBvf0j0nZXHIoC6cu09P9hvQ6X/fTySBPp0/VUqCvnynaZm1vNbyENr4S6X9arCuk4bvyz9uuejXWQ/A6rlN+2arrjDidNj723L/LOl6Yvs6OetmPwybljXtmx33Egdj7EVSnQJagqD6HI2AYHIzO+GzV6BsFpSVwqpS2Lmx7r+XUyDKtqhELvANOFy6hCTZSK5NcFjt2ACfvVw9b6vn7t5py28D3UbKvBWVwIAjZREm1UjeFcE62bgUvni9eu7WLpD/rS5adqlacyOl80a/Q2T9+iQn+7MngnWyao4o4FWlMn/rP9/9Yz/televuR77SETPc+XgS6KRXBvfr0pXW/Dle+LkBnO3eXndf89OicII5q7PgfJ2R3M664J14lZKY4yyGdV7dvu6uv9eOg86D63WE/0PgzbFzWruAodgS3mGNxauZe7KzcxbuZn5ZVvYVlF3gKplXpqhRa0ZVtyG4cVtOGRQZ1oX5CTbwahNsE62lElAr6wUVs0Sx9epqPvvtehQteZGQfFoWXep3KoAaAKN5F0RzN1Xi6SCJdiv6z7d/X3JNj2qdWyPveVFct9vZnrCFR27/ENYOq1aT2xcUvffsyzo0L9aT/TeX+YwAnadfkejJkEUIZiULWVivFTukJScnYacQmhTBO37VRl4nizaZqI06iS40BbMyYYlsGk5ODuqNnyeOGft+0LbHvJ3PJc4XnwMHTcjc2BZ8t+/+kTuI2S2yxyl8yX12GmQ1DjC1+e7ueL74GVEgYIEC9YuhJ0b5L/7vqy5/DYyd/lVrRDdjMxbksrLGkrtc6t8s8xd+Ra5z2FZMnctOsh7Ouk8+XNupdR0NweFWxe1z61ta+CrT2W/OoGeaCGR0I4D5P83egIQp8OyqlvPLt+wg2UbdrCj0iXjeuSkbFrkpujRvgXd2xVgWxau5+P7fvNxLuqi5rnlOVK6vHU1ZHbI/5/OE/uk4wBZe1BdimX0RLWecCrkrNuxXubO90XH5rcWPVHQVv6c0RPfPLcqtlbpic3V9/5yCqSyovNgOfcgcnoiWo7G7vCqIoERmbjYEKQym0u0PUzM3DUe3wf85q0kGovvkdhSqGxj9ESj8H3/f9cFLDN3DcPoicZj9ETjiZGeiI+jYTAYDAaDwWAwGGKDcSMNBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMIROWvcAvoHrgGWBnQLfg52bILMT3Eqw05CTD/ltIZUDvg+eI//dAG5G5siywPOgYov847kyl4WdIL+1/FnfBy8Ddo78+eZM7XXkVED5ZnDKZe7SeZBTAAXt5H/3HPlPO3rbRzm+J3MUzF3ldqjYKnOID+l8yGlRve5cBywbbBPjkH3pQ6pqHZVvkflzKwBL5i6/lcwffH1/N3dq6gmAHRtkvzoVMkfpPChoC6ncqj9fWf3fmztuRubNsmUN7twocxfMUTpfzrpAB9fc380dt7JaZ7qVYp84FaIT0nkydy3ay5+tvb+bM7V1bGaH6IlMOeBDKg9yW0B+G/nfPQeosb+bM54Hfo09WLFV9IRTLv9/Oh/yWkFuofz/Nfd3RIjGDvAcUQ4bFsPyj2BVKZSVwuq5ULntm38+lQOdBkG3EigqgeKx0HU4YIFFpCY4q/g+4MvPu2kZLHkblr4PS6fBpi+r/vdatOgAHfeCjgPkP/c6Sv578DtoLniuzJubgbJZUDZD1tyqUlj3mSjY2uS3hW4jZc11K4Eee0ObYvlGc1LEwVqp2AYrPoaymbBqtszfpqW7/jutusqcdRsJRaNk7lq0b35zF/y829fB8g9l7a0qlfnbtvabf96yoF0fmbduJVA8Ws673BbNb88Gc7dpWdXclcq8rZ4jgYHa2CnoOLBqv46Uees2ssqApvk4u74vhoqdhrULZc+umi1rb838aoOlJul86DIEuo2q1rFdhlSfm83F2Q30gOfJnK2cXr1fv/qkOuhUk7zW0G1E9XnXY29o10uc4+bkdATnU2YHrJhepWdLZf42Ltm1fVLYqYaOHQU994HCjs1XT+zcKDZx2czq827rql3/nbY9a+jY0dBjnDggEVh3lu/v6retAN8DLCjfBDPvg9KH4KtFjf9eYScYcRqMvQg69E+2EvZcUZablsOHd8C8J+tefPWh414yd2MukHn0vOQq4WADl5XCjLth/pRdGyn1pWg0jDobRp4l2Tas5Crh4MD67DWYdR988qJE9RqDZUPfg2HUuTDkOPl3Sd2vIOeR78t6m3U/fPnOrhVtfUjnw6BJsl/7HBAJRZI1AkOvcjvMegBKH5QAVGMpaAfDThY90WVosucu0BNbV8H0u2H2w+KkNZY2PWDkGTJ3rYuqv59EgnWxdiFM/w/MfVyMvsbSZRiUnCXnXV5L+XdJDYgGOvbLd2HGPbDoOalKaQyWBb32g1HnwLCTwEold81BdSZn4bOiJxa/KfusMaRyJZA8+jzof6jYdZrOOj2OhufCljJ4588w+5FdR1SaQv/DYP/vQ68JosyTYvgFDsCaBfDW72QDh/nrs1Mw5EQ46kZo0TFZGzpQip++BO/cJBHRMMlrDWPOh/2ulfRvkozmoExl1v0w7RbJ+IRJ6yLY+zIY/22ZtyQZfq4jztgHt8JHd+06a9EUOg+BiVeJAeh7yVp3ngs71sE7fxEno3J7uN/vvT/s/z3od7DMXZIMP9+TiPt//wQLpjbeWNkVdgoGnwAH/AA6D0rWvAU6dvHbsu6WvB3u93MLoeRs2P86KOycMB1bVRY753F4/++wZl643y/sBOMvgQlXSqlV0vSE74qO+OB22LIy3O936C/zNvpcyeYqnju1jkawEKfdCm/8JnwHozajzoGj/5iMRek6Urv93DUSXcnmry23EA78IUy4QsuiDB3PkVraZ66UKHw2KWgHR/1eDL8kRPx8TxyLpy6VlHc2adcHJt8OPRMQIAjG/8Ub8PSV4SuO2vQYD5PvkvR53NdcsG+m3w2v/lRqkrPJ0JPg2L9KpDnujprrAD68+Tt4/2/hOhi1sVMw4So45AbAir+ecB3IbIfnrpUqgWyS2xIO/yWM+1Yy9ITnwuYVMOVSWPZBdmW1LoLj/yEB5bjriYDlH8KUy+T6QDbpOgJOugs6DVQaIFDnaHgubF4uBkvY0eTd0boYTrgF+h0S30Xpe7ByJjxxUd018Nmg00A49mapk4xj1Co4wGc/Ci/9sGmp74Yy8Gg4/ha5kBpH4yVI4b7zF4mKNrZEqqFYFoy7BA7/dXyzG0FQ4MUfShZIFel8MfomXBHf7Ibnwva1MOU7UjagisJO4mwMPi6+paO+J2VlU74tJT+q6DRQnNxuI2KqJ6p+34uel0Be2FnH3dHnQAmutOwaT2cjCB5/cDu88evGl0g1hpKz4Zg/xTeQ7GZkz776M/jozuwGj2uSypGKnwO+ryyQrM7R+Phf8MoNahdiTUafD5Nu+nqnkqjjeXK5/b9/hrd/n93oVF1YNhz5W9jncvWym4LnSDeQJ78Fn7ygZwwF7STyMmhSvBSw68C21fDI2dnPYtRFuz5w2r1SRx8ng9lz5cLo4xdmP4tRFz3Gw+kPQEH7+F2gnPMYPH9d9rMYdTHsZDjxdllzcdETQQDtzd9JObIOPWGnpGz0kJ/GK6DnuaIrnr5CKgV0kNtSDOaSs/TIbyyuA+Ub4dFzYdk0PWNoXQQn/0fOvLjsV5A1t3YRPHZu9rMYddF1BJzxILQqyrqzocbReP3XcgDqpt+hcObD8VAiQUuzR8+GT1/WPRoYf6kchnFQIq4DlVvhvhOkS4NOLFsc3LEX6h1HfXEdOfjuPRa2rdE7ltyWcNZjklGL+n4F2bOfvybKI9tloXuibU84/znJ6MYl2vf+PyQYpZsee8M5T0lzh6g7ucFl+aevlIvyuhl5hjhqEP3gShCMeuBkfYZyTQ77Jex3je5R1A83A1tXi57Y+KXesaTz4LT7YMAR0V9zIM7tio/gwVP1BVQCWnaG856Fjv2zetZl39F45QZRIFGh10Q4d6r0wo5qetz3xGh55Ez47BXdo6lm1Llw/N+jvZk9R9qu/ucIuQwZFY7+ozhrUXbSXAc2fAH/OVJtmdnuSOeLs9F7v2g7G54Ln70s0b1dtbzUQcsucPErVc5GxDMb7/wFXv+V7lFU020kXPiirL+orrugvfmT38r+nYKGMHQynPIfIt2Bz3MlGHDPsdI6NCocdD0c9CPdo9g9riOdzP59eNO6XYaJnYZT74GBx0R3v4LohmUfwIOn6KvuqU1BO7jgBek+mqWgVHYtxrf/GC0nA+SdiUfOAnbxTkJUsGy5VBUlJwOk3vzVn+keRd14nkSo7jshWk4GyB2R2Y/s+n2OKOA6sLUM7j0uOk4GiDHwyJlSwhUVA742nivnyuMXRGuM29aIIbVjfbTGVRPfl04rUXIyQDKhD5xU1ZY4onvWstRcXG4o86fAM1dF18kIHo978NRoORkAb90o9x00vTqwRzwHdm6AeyZFx8kAGdcTF0nbcB2lg/XBc+QO1UOnR8fJANH39x0PW1ZUNZMIn+w4Gq4jdfFv/jYrn28yn78WjRR9Xbz+q+gpj4Bpt8Cnr0TTcLFtNd2RGoPvwzNXyEGTpc3cNDwpIdBdLrUrKrdXp5mjZvR5Lmz/SoIXToXu0XyTzctFsRFBo891pGTlxf/TPZJds+wDePa70czgeq68oTTjHt0j2TWzHhBdEUWjz7LkHtDS93SPZNe8fL28QRFFPWHZ8PDpapvS1Be3Eh49R3RY1Nad70mlxYOn7voRat1s/0r0v5+deQv/BPU8aRH37HdD/3SofHiHdL9yM7pHUo3ryAua796keyR14/uSbdm+LlrOhufA3CfkbZGo4rniCBGxaJXvS7vpdZ/qHknd7FgfTaPPTsHUy6Fii+6R1M2qUjlTouSk+T54GZj6nehGb0Eeufv0lWgZfZ4jjQZe+6XukeyeN34jjm6U9ITrSKBx1gO6R1I3vg9PXw5eZbT2hu/BuzdLB8yoUrFVzpSolU9ZtmQft3+leyR1s/5zCXJnYc2Fr7XtqglV2SKuMfi+9C32vehsZsuCZ6+OznjqYudGePx8IhMl9Vx53fuF7+seyZ75apF0h4mK0ec6Uj4QtRLHXbHgaSnLiIrh4rkw41744nXdI9kzb/9RHMmoGMyWJVll3RdJ68OzV4GzMzp71qrK3GZ26B7J7snslHFGJTjge1KK+cxVukeyZzYtg5d+HJ3yM8+B9V9IaVfUWfwWfPzv6GQ1XAcWPAPzn9I9kj3zwW3SNTFkPRHuCeA5ElGOatlPbTYslgehorCZPVdSzWvm6x5J/Vj2gdRWR8Hos1PSdSVKdwt2x/t/g1VzImL0edJ3PypG1J54/ntQvlW/Egnee3jlJ3rHUV/cyiqjLwJnnevAl+/B9H/rHkn92Lpa1l0UDOagZCoKXZLqw/IP5YFe3fsV5Pf34g9gS5nukdSPGXfLC+WRqLqw5PxQ9Z5SU3n1Z9KiXfe68zzpgPn8tXrHUV/8wB4Id97CPTl9Xw7kOPHRXfK4kc4F6XlSV/jW7/WNoTG88xf9xrLrSGmDrrcyGoPnShmQ7rajngvv/U1e/o4LO9bDqzfoT43bKXjxR/rbEzaEVbOjERyw7Xhkbmsy51Ex7nXPXcUWaRcfJ974DZRv0jsGz4HlH0HpQ3rH0VCeu0b/Wec64vRE7eL87qjcBi/8QP/cWRa89gspNY8L67+QUtsQbeLwHA03I/WsW1eH9kkl+L5Mqs4FaVlS2hD1VHhttn+l33BJpaN9p6UuVpXCknf0Omp+VXQ0bsx5TNaeLkPV86S0YeEzeuQ3hQ9uQ2vJo5uBRS9IPXDceOcmve9qeK78/uKmJ5xymHab3mCeHVM9sWGxlN3o1BO2De/fok9+Y/nkBdiwRG+2fucGsYvjRsh2XXiORiqnSonFkHlP6b2kk9kBcx/TJ78pfHSXvpICz5USpLiUEdTm/b/ry2q4GWm3G6dIS4BbCR/coU+BWMidlriUm9Vk0zJY+Ky+coxUjpSIxpHPX60yXDQ5uL4ntedxZPq/s9bRZo/4vqz7T1/SI7+pTLtFo55wJDCwcYke+U3B96vuHmoKrARljlHsRrgndmyQ7F9IeiIcC9FzpRPB2oWhfE45niNdKHRE5t2MpOUrt6uXHQabl8MnL+mJuFhWdNs71ofPX9PXNCGVAzPv0yM7DEof1HffwHNh7uN6ZIfBzPv0PODn+3L5O66BAd+X80aHg+lmYNHzUjoYR3ZuhAWaHFzfq/q9xahUryYrPpYMoI51l0rDzHvVyw2LeU/ou1diWfEr1avJrPD0RDiOhmVFu11cfSh9SE9aPJUTn8vzdfHZK3peWffceM+d78m60+HgbvxSFFhc2bpKuouonjvXEYMvLo0HdsXiN/U4uL4HM+9XLzdM5jyix8FN5YhzHWdKH9Tj4Nopyd7GmZn363GUtq+LR1e9uijfDAufU+/geq68hbJ5hVq5YbJyZmglriE5GjYseSuUT2lj3ad6ykh2bpJXhePMkrf1lE+tnqv/kmFTWfK2egfXrYTPY6w8Ar54E+VpcdsWByfO+J4eJ81OxV9PbF0tdfOq8TwxXOLM0vf03NPYsETeHYkzS95Wf4/Uc0Su7s5NTWXJWxqCyH6Vfoo5X7wRSkYoHOuwcoeewzdsVs6QA10Vvi+XguNY612TDYthq+IXpd1K6fccd8pmqZdp50Tz9fSGsqpUvfK17OTMnWonzXNh9Ty1MrPBio/Vl4puXBy/S+C1ccrVd7jznHhnbgPWzNdQnmxBWalimVmgrFR9FtJOJ0NPlJWKvdBEmu5o/M9Yjmn9Y03KZqo1+r1MfN7N2BOL31CbnrRzknEI7tyovq+7lRAFsmq2epmuk4w9W1aq3klb/7kYm3FHteHiJsRYBlj5seIyFisZBp9bCes+USvTTiVj7r5aqOdukI4gYtisKg3lrGu6o+FlJBOQBMpK1XZ3sHPkpegksPxjtelJKyEKBGDFdLVlLG5GDt+4U74ZNi1XK3PdJ/F5tGp3rJ6jVp6bSY6xrDqTlpTAAKh3cO1UcuZuxXT1BrOOYE7YuBn1jYq2lMX7Hl/AV4tC6ZrVdEcjlZuMxQjqfw7LgrUL1MrMFttWK47yVSbHSVO97r5aFJHXZkNgpUInzU1QUKViK2xcqk6enUqOnlg9V628RM3dHPX3+VQ71dli1Wy1TtrmFRLMSQIrZ6jTeZ6bHD3huaE4aeGEoOPacq82OzRcBl//hXqZdZHKhbxWkNca8ltX//e8VvJPfpvq/57XGj+vNRS0hfy2UNhBbcV3xdb4X1IL2LEOLIUKZJvi+zTZZPs6deWOvh/Pd0fqYvtX0K6XGlmWnRw9UbkdMuWQk69Opg7dlA1U7x+3UnRFEtixTq2TlqSzbsc6QFF5v+8la+5CsBfCcTSSUHcL4vH6ntrNrMLL7n8o9Jz4NefBL2hX5Ti0hryWkFuIlcpt0Gc1vi8cz0dw6sIpV5cN8n3I7FQjSwVOuTL9gUVyzjpQvw6SNHdupVpHIynnneqfw0lAmWOA6v2TND2hzGLxk3XWOeXSJKkJTxiE42gkJbKshexbSX6fA2H0+VXGrLVro9apwG+KErBSWLktGv/3G0rS1pzvq3E2fD/+Xc5qYlko9TR0vW6cBJK2Z1WSlLlTvX+SdNapbriTqLNOoZ7wMXNXi3AcjXReKJ/RjmWrrx9N5QLZfRXcevVn8OrPsiqDbiXw7bezK6Mm6YZlXyKNnVaX0bBtSCVkv0JVyZnCSFWS5k7142lJ0ROgvmNXUuZO9f7R8UBgtlC+XxVm7LKN0uYNJEtP2E23i8OxqnMURrKzSU6Bepn5bdXLzAaq5y4paw5k7lRG3vJaqpOVbXJaqG1CoDJrl21yC9XKS9KebWCZaZNJytyp1hOpPD0vuWcD1XOn+nzIJjkt1CW+sZOlJ3JaNnkPhdDe1oWOezX5M5Gg40D1Mlt2Ui8zG3TcS21qN68VFHZUJy+bdByotr1tp0HqZGWbzoPVtVW20tAxIXNnWdC+nzp5biY5eqJ9X/XR5aTMneqfw7bVrvNs0nGg2m6B7fqor/DIFp0GqstqpNLQeYgaWSroMrjJnwhhFfnQbWTTPxMFuo1U/GCfC8Vj1MnLJt1GypsqqmUmgeIxaiOkhR2hZRd18rKFZUPX4WrLzopHq5GVbdr3VRt1s1NQNEqdvGyi+txxM8k564pK1LfWTszcjVLbnTAnHzr0VycvmxSNUVs+1XmI+vLKbBCSrdB0R8NOQ/dxTf5MJCgqUXzpzoceeyuUl0WKx6o1lj0HuiXAcLFT0GWoerlJUL4d+qsvJ2jRHlp1UyszG3QrUSvPSpCT1q1ErbFsp5Mzd0Wj1RpgbmVyHNzi0U3q/NMoikrUyssGLTurrxxJ5+mpkAmbkOyEcFZtu17SJjXuFI9VmxK309BrP3XysoWdDiW91jCsZByCHQeqv+jpOsmYO10/QyLmbpT6F87z20CbHmplZoPi0erK9UAydqodw2xRNEptOY6dk4yqgVbdoEUHtTLdymSsOx0/g+8nQ090GxVKWXd4Oz7uUYOcAj216y07QZvu6uWGSZdh6i9H2qlkZIOKx6hvW2hZ4lTHneIx6ssw3EwyDJfu48QIU03c5y4oAVN9wTi/tZS7xZm2veSBV5VYlkRlVTqG2UDHvrFzkqNjXYV3IEGM87ifdVD1MzT9rAvH0XAzMOykUD6ljUGT5BKPanwf+h+mXm6YDDtZ/UYGSYnG/SAcfqr6ntt2CvodAgXt1MoNEzsFw05RfynXTsPw09TKDJvWRdBjvHpj2c3IWRFn+h4sjShU47nx17HDTtbzHkhuoTxaG2eGnaw+qGJZkr2LexZyxGnq70ukcmDoSfF2cPPbwoDDQpm7cByNVI4YTDraw4bFqPPUdv4J8D0Ye7F6uWFhp6DkLD1OmpuBUeeolxsWrYuhz/56DiM7BcNPUS83LPodoqfrmGVJqWjPCeplh8WIM9Rn0UD0xMBj5J5LXBl1jnqDD6TcaPT56uWGyejz9HQxch0Yda56uWGR3xYGH6vnTRDfh5Iz1csNi+5jJROoo8Vxi/Yw4HD1csNiWHiOUni7PqdFfCN9HfpD3wP1GXzdRsT3st+g4/S1mU3lyJqLa2R+7EV6DL6AvS+Lb4/58d/Wk0UDMTTHXaJHdlOxUzD+En1tKy1bgjpxpFU3Oe90GHyWBW17Qr+YRub7HgTt++g5b1JpGHi0BHbiyKhz9EXGLRvGfSu+Dx+Ou1RPYAAkcD3+Uj2yw2Dvy0KzT0LUNj7sd008DZcJV+pbjCCy9/uePvlNYb9r9KTDA1I5YrDHjdxCOYR0tcCzbHGw41i212mQRIp0ZNGgKi1+ohh+cWPIiVI6peuctiyYeGU8DZe9v63uEfpd4Tqw73c1DqAJ7PtdfYGBgL0v0yu/MdhpmHgV2haeZUl706GT9chvCq2LYfjJ+s4aOy2Zdx1dJZtKv0NDfXskPEfDsiVFNeL00D6phA79YNTZehVfKkdSo3F75GXQJLkYqbNftJ2CiVer78jRVCZcof+Fbs+BQ34Wv37fh/xUv9GCDwddr3kMDSSVCwf/WG9gwLKgsJNESeNE6yLJoumsuU6lJfPee399Y2gMvSaKwaUrMADyexv/rfg1Xhl7IbTqqjeA67lw0I8hna9vDI3hoB/pHoEEkQ/9me5RNAzLhkPD1bHh5s99D475s2yMOGDZMPku9IapqnAdmHxHfC4PFbSD4/+h12gJyGsJk/6iexT1p8tQOOCH+l9dtdPQdRhMjFGUdNjJVfXKmveJnZa7SQOO0DuOhnDQ9RIMioJjedgv49VF6fhb1HfW2xWeK3oit1D3SOpHTgvRsTruP9bGzoUTbtU9ivrTrjcc/hu95bUg50XbXnDwT/SOoyH0O1TuBOm2p1I5sNdRcoc5Lky8SloCh6hjw7V0LBvSBXDc30P9bNbY5/KqV5kjkMZPpeWV4wP/T/dI6sdRv5e++FEwWuy0pHaHnKB7JHvGTlc5txHBsiXK3Vn1OyiNoLATTLoJPE/3SATPFcMlv63ukeyZ4tFVpa2andsAKyUGcxxKbUedI12LdDu3IOdtq27iqMWBQ38m2SDdBh9UZYQOiselesuCE2+XeYvCHrFtKXmMw+PMea3hxNuiEQQF0VeTbgrlhe2s03EvOOSG0Ndc+FonlYa9joSRZ4T+6VDp0F8OwShs4gDLhgN+IK0no0zw+42C8gjwPTjub9EvodrvWugyJBpGy/+wYPKd0XAad8exf4Xclupfx60LOyWdRY78ne6R7J50Hpz0z+g4aCDrv8feUo4UZVoXw9F/1B9Vrklwob/PAbpHsnt6TYR9vhOtc8X34agbo19CNe5bMn9R0hO+ByfdFf0SqiN/Kw1qorLubFsye8f9TfdIdo+dkt9vFtyC7GjsoISq44CsfL7J5LSAU/4jUbWo4Xtwyt16erXXh7Y94YQIRQsCLFvm7MQ7onPA1KbH3nDgj6ITVQ4IsmkH36B7JHUz+nwYfFy0FC+Isz3q7Gi/D3HEb6Fdn+jNHcDhv5IH1aJIKlcctFRetAJSUFVCdae8JRRFCjtVObcRKJmqiWXJ7/Okf4kDHkW6DItGyVRt7LSUUB11o+6R1M2QE6NRMlWboPNZlJ8yOOh6OYuzoCeyY/EEJVTnPxu9yEEqF858uOo164gtRpAN0qornPOkOERRorATnP9cdEqmamOn5YGZ42+JnmHQdQScOyV64wqwbNj/OrmkHjWGnADH3Rw9xRvge1IOF8VHwQ78oUS/o7hfQcZ13tPRC0rZKTj539Bzn4jqiRQUdobzno1e6V5+GzjvGWjZNXoGH1Rl08ZLQC9q+6JDP7GbUhEpmaqNnZIuj1G8r9HvENmzUcrc1sT3YdKf5SG/qLH3ZVJNk6UgaPZCq6k0tOgIF74YnTaQ6Xw4/QHp2hG1A6Ymdlrujpz9hJSKRIGWXeCC56WUIAp3WurCsmHkmXDszdHJHHQdAec/IxG0KK87kDKgKPX+HnQsnPwf8Imm4gVZZ5YNZzwcrXcO9v+e3L+JMnYaclvBBS9IfXAUsNNw4p3SdCDK+zWVho795WyJyltCBe3Ecey0VzQdtAA7JRHmk/4VHX3Wob/sg7zW0XTQanLg/0kQIyr0PUjOX8uKTmltbQL9dfI/JfMSFcZ9C47+Q1ZFWL6f5TChm4GdG+De4+CrT7IqarfktYIzH5UIVZSVR008B9YsgAdOgu1f6RtH214SZWldHG3lURPfg/lTYYrGB3tA1ts5T1U5GTGZO4A3fwtv/1HvGEaeKZf6fKKrPGriuRK1evIiWPC0vnFYllwWjtObC64Dldvg/hOhbJa+caTz4bT75J2WqAQq9oTnwIYlomO3rtI3jpZdRE906Befs85z4Ys34bFzILNT3ziCYFRuq/joWIBpt8ArN+jNNg8+TrJTlh0P286vyrg8czXMul/vWPb/vrSyzTLZdzRAlIiXkQU5/d/qF2Xv/aXDSauIpnJ3h+vA9jUw9XJY/JZ6+aPOFW83lRevAxAkhfrVInE2Vs9VKzuVIxHlA34AWPE4AGvz2avwzFXqjZf8NnDkjXL3wfejm8nYFb4nCu/jf8GrP4PK7Wrlt+0lzlnv/dTKDQPPkfl747cwTUPr7O5jpQSuXe/47dfAUXvuGpg/Rb38ISdKeWPcDGWQdbZpGTx1Caz4WK1sy5Zy1UN/VmUox2zuAJa+D1O/Axu/VCs3txAO+4Vk4D0vHsGogECvzX4EXvwhlG9SK79VVzjuH7CXmvbsahwNqJ7YL98Vo3nT0uzLzC2UyN74S+QwiZvyCAjGPutBeOUnsHNj9mW27CLvZOx1ZPyMvZoElxH/+yd45y9qshtdhslFyM6D4hMV3RWuA045vPgDKH1Ijcz+h4mh3KJDPJVugOeKgzblMvjynezLsywYc5F0XLFz4mfs1cT3Jasx5VJY91n25aXz5EGyfa8WRyeu6y5wchc8Dc9fB9vXZV9miw7yhtHQydXy44jnyNjfv0WyuU559mV26C8X+ovHxFe/guhUz4VXb5AAiwqTste+cNKd0KoovnYdyLrbsRGeuQI+fVmNzJFnSLOmdIEyPaHO0QgIshuv/gxm3ANuZXbk9D9U3vNo1S3eC7EmngPlW+CF78O8J7MjI5Ujr7sf+TvIKYy3wVIT34O1i+DZq2DF9OzIyG0pvcYP+IGU+yRh7gLj4dNX4KUfwobF2ZHTsotE9UadE++gQE2Cn+Ojf8JbN8KO9dmR03Ev6dPeZ/94BwVq4jpAVXbjozuzV9bSa184/u/Qrm+8IqK7w3OgYiu88APRE34WLsdatjgXx/wF8lvF1zmrjedKEPSZq7MXIEjnSxT+kBuk82Ui9EQQSH5PnNyvFmVHTov20rlx72/LOk/Cugv0ROnD8PovYOvq7Mhp11uqU/Y6SnlQQL2jAdWLcvs6+OA2mHlvONGXdL50qNn3GnmrICkGS02CBbLuU5h2K8x5DDI7mv7dgnby0vHEqyWtFrdUZH1wHTnUl30A7/9dIghhtF9s00M6cYy/RLJocY3q7Q7XkfWw8DnZs8umhfPdLsOk48XIM+JTY9tQPFfW2awHxGgO466aZUlJ6IQrJesYrO2k4ftQvhk+vEMCU2GU8aVypMnAxKvlIcMkzl2gJzYth/f/BrMfhYotTf9uXmsYcZrc/2nbM5l6IjBgy0pFTyx8NpyAaKuuMOYCOe/y2yYjIFAbNyP769NX4INbYcnb4WQ4Ou4lzlnQujaResKRAOWcR+W8Wz0nnO/22FvK8wYfpy1jq8fRCPD9qmiLL/XgC6ZKynzdZ/WPwrTpLs+l9z9MDsDcwmQ6GLXxPDmonHJY8AwsmCIHY30VsWVBhwGSth1whCxCOwVYyTwAaxIokh0bYPZDcvelrLT+F+7ttLykXTwGhp0ikeSkRFf2RKBINi6F0gdg6TRYNbv+RkxOgbzZ0X28OLZdhlZ/M+kEP2dZKcx+WOrB18yvf5lGflsoKpGHvErOgTbFzWfuPFfOpS/elDsIZTMlalrfexytuoqe6HewNBnIb9M89mygR92M6IlPXoBVpQ3LTLbvK3M38GgJ5KVyRXcnzcGoTWBHlG+BOY/AF280TMfaKeg4UJzZoZOl/arvJ982gWrnfcsq0RNfvit6or5l3+k80Q3dx8GIM5IbENgVwZm+doFkOZZ/KHdM6xtQzmsN3UZAzwlSIdCut3Y9odfRqEnNRZTZCavnQdkMORAz5eBWSO1xTr705y4aDcWjqtv6NReFuytq/uzlm2HVHPGG138hkRgvI5e5cwvlgafisbJxcwvl4POcZjx3NdbdtrWwcoY4uzvWiwHoOZIpyymQqErxWHEyUjnVjnJzUBy1qf2zb1omhvPqebIGnXL533MKpKSs82BRGu37iYESGEBJzP7sicBotmz57+s+g5XTYe1CuTzulFe9RZQPBW2lI033sdVvEnmOlFwkPSCwK2r+7E6FKOMV02H95zJvTrnoiXSenHXdRklAoLCD/H2jJ+S/V2wTh6Nsluxdp1x0RSpX1l2bHqIjupVAXstv/v3mRs2ffcd6WDlTnN1ta2UdehmZt3S+dN0qHiuGcjqvSse6zcNI3hU1HfrNK6v0xFy5AJ3ZKbognS/2SOdBUDxO3tWxU/K/NRfnrDY1f3bPg42Lq4JTC6TxQxCgShdAfmupDug+Dtr1kn/vuVWt1/Xrieg4GrXxfdm8Vurri8z3qiMNzdFIqQ/B3Nk5X19knludOovA4osk/5ujGusrMKp9r/kq2voQlKEF70pA1dy5ybmzki3cTJXzUcOB8L2qzCXJj7w3Bbeybj1hpZIfeW8sQZCpdsmi0RN7xvPkXKtth3iu/PtUrr6xRZ2gDbhdW08YHbtH6tITQeAuonoiuo6GwWAwGAwGg8FgiC0m1GMwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXSMo2EwGAwGg8FgMBhCxzgaBoPBYDAYDAaDIXQi6Wj4vk/G9Xb7ZxzXw/V8RSOKEZ4Hbmb3f8bNgL/7+W2WeI78szvcSvDNuvsGrgOeW/f/7tdjXTZH6jMvvm/W3a4I5mV3eHtYl82V+qw7oyd2jecaPdFY9rQffd/oibrY036sj+2nibTuAYA4Fp4PKdtie4XD3JWbmb18E/NWbmbZhh3szLhkXJ+UbZGfY9O5VT7DilszorgtI3u0pVOrPABczyNlR9J3yh6eC5YNlgXb18PKGbBqFpSVwpYycMrl0EvnQboA2vaEohIoGgVFoyG/tSxQC/lOc8JzwK7aAptXwIrpsKoUVs2BHesgsxN8F1J5kNsCOvSHbiVQPBa6Doec/K9/oznhZiCVI2tnwxewcrqsuTXzoHyzrDvfh3Q+5LWEzkNk7rqPhY4DIZUW5yTVjOeufEvVvM2CdZ/BxiXy71I5Vf/kytpr2wO6DIXu46DbSFlvwTeaE74v+9FOQ2aH7NOV02HVbFj/hexXt0L+93Q+FHaSNVdUAsVjoHWRfKc57tnAQLFs2LkJVs6Esply3m1eUTV3lbLmcgqgdXGVnhgNxaOhoJ18wweanY6tsV62rqqhJ2bDtrVy1nmO7NWcAmjft2rNjYWuI0R31NTTzYngnPI92LgUVnwsc7d6rqxDZ2eVnsiD3ELoNKhaT3QeLOuxOZ51UP1zuw6s+6Rq3c2GtfOhYpusO5A1l9caug6rnrv2/WWfRmTuLN/X53Y7rkc6ZbN0/XYe+Wg5L85bxZfrdzT4Ox1b5nLQwM6cNrYH4/u0/993E02wgFZMh1n3w6cvwdbVDf9O214waBKMOV82eUQWZlbxHDn0P3sNSh+AxW9D+aaGfcOyZb6GTobR50Grrsk3YHwfqIo4zZ8Csx+B5R+K0dcQUrmiiIefBiPPgNyW8t0kO7qBoeFUwIKpUPoQfPnfhkc9c1pA731hzIUw8GhR4Elec1DtkG5eATPvhflTYf1nDZ+7Fu2h78Ew6lzoe2Azmbuq83z1XJm7T16UeWworYtlvY0+T5zd5qAnXEeMtSXviI794g3Ysb5h37AsCVANORFGny9Bg+YQYPFc2V8Ln4PZD8LSaVC5rWHfsNPiqA0/BUrOEmfXc8FOZWfMUcD3AAsqt8OcR+Wfsll7ztzWJqcAuo+HkWeKjZLOle9qcnS1OBqO55G2bd5ctJa7/ruYaYsbuHl3Q5+OhVy8Xx9OG9sD2yJ5DodblbKd8yhMuwXWLgjv2z3Gw8SrxfHwvOQdhp4jht5H/4SP7pSMTxhYNux1FOx3rcxhEg9D35Po3fv/gJn3QcWWcL6bzocRp8G+10CHfmI8Jinq53lirKyeBx/cBguebrjCrYu2PWHsxTD2QshrlTxHLXDcl7wD790MX7weXjlK256w92Uw9iIxmJPmcLiO7KP5U2HaP8RYCYtuI2HCVTDsJPl9JFFPuBmYcQ98eAds/DK8b/c/FCZ+VxzdJAamfE8yFR/cCtP/Azs2hPPdVI4YzPteC12GVJ+rSSHQexuWyFk3+5HqjEVTyWslAYKJV0HLrlr0q3JHw/N8lqzbzrWPlTJnxeasyenUKo8bJw/nsCFd8HwfO+7GS7AQF78Nz1wJm5ZlT1bnITD5TknFJcF4CQz/mffCKz+V0p5s0e9QOPFWKOycDGcjcGzfuhHe/1v2akAtC0adB0fdKCUISTBePAd2boRXbpDAQLaO2nQ+TLgSDvohYCdk7lwJBEz5Nix9L3tyWrSHo/4gzm5SjBffl5KyKZfB+s+zJ6d9X5h8B/TYO3syVBL8/uc9CS/8oOHZi4bQc4Lo2Dbdk6EngiqBd2+Gt/8QnpG8K4afApNugpzCZJx1rgNeJbz8E5hxd/b0hJ2WQPLBPwYspXOnzNFwXA/bsrj97S/422ufUbmHy95hcUJJEb85cRgFOan4ZjeChfjS9RJlUYGdlijzQdfL/x/XDe05sH0dTL1cIqIqyGsNR/5GUuVxzm74HqyZD09dGm7mbHe06Q4n3FZV2hLT7EZgsMy4RxzbsLI/e6J9XzFeuo+Nb4AgiPJ+eAe89suGl+U1lkGT4Ph/QH6b+EaZXUfusLz+S/jgdjUXuS1LMkOH/RKsVLz1RPkWePZqWPisGpk5BXDIT2Gf78S7jM/zYONieOoSufujgpad4dibq6sv4hggCPTbkv/C01dkN3hck86DYfJdSgPJShwNx/NYtn4H1zya3SxGXXRqlcfvTxrOIYM6Y8XNcPE9+PI9mPod2LxcvfzOQ+Ckf8rijKPBPOt+eOnH6oy9mvQ7FCbfDgXt41XP7HmAJ1mMd2/ec4eVbDD6fDj693KfI04K2HPF2HviInUGS03sFOz/fTjoR+isyW0UrgPbVotjm80sRl0UtINJf4FhJ6uX3VQ8Vy7ZPnVpdrMYddG+L5x0FxSNiafRN38KPH9deKU+DaHnBJm71kXxOusCR/a9v4mucCrUj2H4KTDpr3J3LU5OblCe9/KPpcRMNXZKAslBdiPLtl3WHQ3X85m5bCMX3v0x2yo0GCw1uGHSYL61f1+tY2gws+6HZ67W22YwpwBOfwD6HhIPJRJECl66XuriddKqK5z/HLTvEw8lErRufPRs+OxVvWPpVgLnPS2XxeOgRDwHMuXw0Kmw9H29Yxl5Bpx4O7FxNjwH1i6E+07IbslKfTjgB3DIDXrH0BB8T5zaJy/W294ylSOR0qEnxiuj9taN8Nbv9Y6hoB2cO1WizLHQE1XBqCcvgflP6R1Lx73ggufiE9BzHchsh/tOlM5vOul3KJz5sKy5LDobWXU0XM/n/c/X8a37plPhRKMf97WHDeC7h+2lexj144Pb4aUf6R6FkMqBk/8Ng46Lh7PxzFVyaTkKFLQTZ6PzoGgrEc+V7hYPnKTfUA7oNBAufDH6JS2uAzs3iKGsqsxsTww/VSKlUXc2PAdWzpJ1pyPzuCv2+Q4cpdn4rA++JxdHn74iGm9eWDYc93cYdXY8nI1XbpAGF1EgtyWc86SUPkb5rPM8ydo+chZ89oru0Qhte4qeaNk12kEp15Ez7p5jJLASBXruA+dOkbuRWXI2snYSOJ7HrGUbueT+6DgZAH997TPu+u9i3cPYPb4nRnJUnAyQSNmTF8PiN6L/ANYLP4iOkwFyIfi+46WPuKs3q1cnnifr7uEzouNkAHz1CdxzLFTu0FPCVR88B8o3wr8OjY6TATD3cXjyW4Af3cfDXAfWLoqWkwES5Hn917pHsXu8qkxGVJwMkHE8e7V0u/IiMqa6ePO30XEyQLrRPXgKrFkQXT3h+/Lm1hMXRsfJALnfcM+x0qY+qnPnOXJJ/t7jouNkACz7AB46XZzHLO3ZrDgajuexalM5F979MeWZ6B02v3thIc/NKcOJ4kHoOvDFm/Dsd3WP5Ju4GXj0HPhqUTQ3s+/Be3+Hj+7SPZJvsmM93HusKJMorjvbhqmXweK3dI/km6xdIOVIUYyQ+lVG/MNnqLvM1xDmPQnPXRfNjIbnShbo/hOi5WQEvPNnqZ+OYmDFdeRx1ie/FR0nI8D3YMqlsOKjaOoJz4UZ98Lbf9Q9km9SsVX2w4510Vx3lgUv/p+e+2d7YuMSySgTsf0QYNmix9bM0z2Sb7Lkv/DUt7NWrZKVr9qWxTWPlrJV852M3fGTKfPYvCOD60Uo0ud78lLm05dHT3kEZHbKhcOo2S2eI68rvxHhKOSWMrlwGLXSM8+RCOTcJ3SPpG6WfSCXDqO2LyxLMo8rpuseSd3MuLsqwhyx89hOyVm3fZ3ukdTNyz+RfRu1ufNdMeYb+pCXKtyMtCb2nGhl0zxXXvd++XrdI6mbHRukS2LUmq+4jgSiPvqn7pHUzZp58MZvo7XmQIKL798SrWqB2sx/SgJTWTjrQrd4XM/nP+8uYcbSjWF/OlQ278zwwyfnkLIjZDFbNrzw/ca98K2SNfOkV3akjD5LHKCoKt6AeU9KNCgqhovnSRTt+et0j2TPvHUjrP8iOlFSz5EL8x//S/dI9syzV0sJX1SipJ4Dsx7U33BgT2R2iMEctZr5134OGyJeArzxS3j1hmhl0+yUvC9SuV33SHbPF69L+W9U9qvvg1shZXpRZ9o/pANblPTEpi+lVC/qvPB9afMcctVFqI6G43ms2LiDP7/ySZifzRqvLVzLlJkrolFC5Trw6ctysS8OvHNTdGpJfQ/e+YscLnHguWuhYls0HDXblq5mujv91AenQqK4UckI+Z40HYgD5Zvh2WuiESX1XFlvUY4q12Tpe3JnIwpGn+tIdu/DO3SPpH58/C9pzx4FPeG5Ulb75Tu6R1I/Xv4JbF8bjXUXdHHcvEL3SPaM50rQMSolVJYtb4xk8xHDsNixQXRayDo21K+lbZvvPz47kvcy6uLnz85ne0UENrKXkahjXPCcqkifZqPP82DDl/DfCNbb1sX2r+DFH+i/c+BmYOFzsPAZveNoCCtnwrRb9Stfz4Hpd0sZRlxY9Fw0jD47JU5Pufo3lRrN67+CbWv0l2RYyJtKusdRX3xfyuN04/ty7+G1X+geSf2p2FJl9GkODriOnBsz79U7joaw7lNpWaw7mOc5EhSIcmltbRY9J6W2IbbKDs3ScVyPj5Zs4OMvo10yVZstOx3ufm+J3rsaniOvCEe9ZKo2a+bBpy/pNVxsWy5t6uwf3xjmPiHRIZ0HYSonXg5awPt/169AfB/eu1nvGBrDtH/obf/oudJM4pMX9I2hMWR2yB0hNOoJNyPnRtRLpmqz8UuY86hmB9eX31/US6Zq89mromd1BlZSaSmVjhsf3SV3SnXiU3VuxIz//inUN0lCczTSKZs7//tFWJ9Tyn3Tlup1NCxbUvNx5L2/6TVctq+TNp5xw/f0GqqeA1++C6tm6xtDY9m2FmY/rM+59Bypn95Spkd+U/j0ZemOpctRs1Py2nwcmXW/XkM1lROtdqwNQbeDm9kZrZbnDeHdv+rLangurJkPS97WI78pVGyV0j1d9yHdDMx9LH4BZBDndvHboc1daI7G2q3lvLlobVifU8qG7ZW8OG8VjqtB+XoOfPEGbFqqXnYYLPtAuj3pMFw8R9K5Ub8AXhezH9VnLNvpeFxirovpd+t7BdZOxzNKBbJPp92iT37FNv0vCTeWyu36HFzPlXa2UWyNWR/WLoTlH+uJzLsZuftYsVW97DBY8Iy+MkPLjreemHGPvkYOqRyY/m89ssPg43+FNnehOBqO5/HYxyuIUqfYhvL49BWkUxpq5u00zLxfvdwwmXmfnpphOw2lD6mXGxYVW0SJ6DBcKrbGr3ylJmUzpQ5XtYPr+1BWGt/AAEi3J0eDc+5mpITGqVAvOyxKH9Lj4Fp2AvTEvXrupaVyoPRB9XLDwq0UR0mLg5uRTolxZcNiWP6RegfX90V2nO5m1ObTl0JzcEPZ9Wnb5tUFMUwP1eD9L9axs1JDtMVz4bOX1csNk0XP6Untrl8M6z9XLzdMFj2n3nAJOpzF2eADWPC0BgXiRfOxqoZQuU06Kameu1QOfPK8WplhUzZLSvdUY1nxn7tPntfT6nbHeskGxZlFz6vXE74HS96NV9OGXbHgafUyPUeCiHHGrYRPXgzlblVoGY1Fq2OalqzC82FB2WZ81ZH59Z/rv7DUVDYuUV+77Dry8mzc0dGS17LEYIo7ZaXqla+dgsVvqpUZFi3aw15HwaE/x2/fD19HuWMS1t3KGeqdtG1r9Tg4YbJjg/p7TZ4HK2LuZICeu3SeC2VJmLtS9YHQVE4yzrpVpaEEB0IpwPpi7XYqnPi0tK2L0hWbGNGjLTkpRVEXNwMrY5xaCwjKSXpNVBexsqz4vJuxOzZ+KQ/k5LdWJ9NOJWPudPwMmfJ4XKC3LOi4F/TYB3qMx++9H1a73gB4ToaN5S4dVDtpW1eJsRl3ymbCgMPVyQvuZySBlTOgZRd1hp/vyu8r7pRvki6Fbbqrk5nKEb0ed1bN0SS3VI/cMCkrDWWvNtnRyLges5bHq6VtXcxdsYUclfc0rFQyNjLIYd5jHKRy1cizEzR3q0qh9/5qywp0Hb5hsnkF7NwEBW3VyVz/WXReda9JbiEUjxGnoscE6Lk3Vl4rfN+norKSLzdU8OH7X/Ls7DKmL91Ifo7Nol8frW58nhvveuWalJWqvWDq+8kwlkHmbqDCdZfKSYbBB7DiY2jVVe3aS8LcVWyBjUuhXS91Msu3SBAx7qyeIyV0Tbxb1eQVm7It5q2IeQ1fFXNXKv45bDsZGxkkTajKyQBZ/KsTYCyDPELXc4K6MqCNS+XwTQIrZ0K/g9RdMo1Kq8K2PaHHeOixN36vfaHTYCzbxncdNu7IsGDZNt76dDlTZ61k3bZvXvwuz3hs2F5J+0JVe9ZPVmBAJal0guZulvouQEmZu7JSGHycOnk7N8XjJfD6sOJjaFOsZu35fnLsusrt4jC179ukzzR51m3LYsWmmN8xqGLFxh3qhW5arl5mNti0TK288i3xe3ypLjYtU1tDmoRIS8CmLyXDoMLJdR15XVg1qVzoNkKcih77QK+JWIUdAchUVrBiS4YZs8p4Ye4q3vp0LV49q1jXbClX52jYadis+IzIFtvWykVJlYGVzUZPNArPlZK9JLBpmVonLSlrDtS+HeQ5EsxLChsW63c0ACoy2f8FtspL06lVXlZl6GiIgVOuQWgWUN3BKO4dk2rilKuLyPs+ZBLioIHMnar+Db4nHWyyTW4h9D1IHIueE6CoBKvKqPU9j7VbK5i7YDVvfvIVKzd8PTiyf7+O9ZejuvFFJiFnHUh7YJWORlLmTvXP4SZMT6gk7k1qaqJ07nxwEjR3mZ2iK5pgIIfjaCi4CH7E0C785bSSrMvxfR9LpceRFIPZU9zjO0kKxHOavJHrje/reUMhW3guyjwNy4IdCu6jddwLzpD3YWqvCNu26dqmgK5tCjh8SNfsjyVMkrRnfcVdp5Iyd6ofV9X1IGo2UH03LCm2CVTtV2PXNQrfq7qn0fiqi1Acjdx09n+Bb3/6Fafe8X7W5Tx+2cSsy/gaqVzIaCjZChvVdbcqo4nZxk6p7dal60XtbGDZKFUgKli7AP5zVPblHPNn6Dos+3ICkrRnVT88l5S5U96OOkFnnfIWrQlZc1C1XxVmcJM2d020T0KxDvPS2d8A67ZV7vJSY5jkpTW8WprObjmYMlT/HKmEzBtAOj+Uzg71wrIgp0X25agina/Qz/ChsEP2xTgVsGxa9uWoji4n5awD9YZEUuYuna9WXpKCKqrnLqdArbxsonTuLPW/q2ySzm+ybdJky8b3fbq1Scakavk52hSrl5kNWivs7w2Q3yY5B2HrYrUPgLXtqU5WtmnTAyxF2TTLljcAkoLKdeA56s+IbNGivXrDPylzp/IdCBBHo7CTWpnZQrWeaJ0Q2wSqfhZFgWQ7JXopKYTQFrjJM+94PsOL2zR5IFFA+c/he9BtlFqZ2aKoRG09rG1DF4VlH9mkaLTatHj7PnLhOAkUj5G1oAI7DR0GqJGVbQo7QWEDLo6HQVGJWnnZQvWZ7TkJmrsS9XcNkjJ3RSVqGzgUdpB3O5JA93HSJloFlg3Fo9XIyjY5BdC+f5M/02QNnZOyKenZtskDiQLDituQcRW+cO650G2kOnnZpGi04kesvOQokOLRamu+LRu6DlcnL1u06qqmlKkmHfppak8XMt1GqJVnp0XZJ4GiEsXGsiUGehJQre9cJzlzp9JYDkjC3OUWSnBNJQVt1WfvskGXYaEE8kKxbvbq0oqcVPyV78gebUnbCn+OVA50H6tOXjYpGqXWAPPcZByCbbqrfdkakjN3On6G3MJkZCG7jlQfWW7THfLbqpWZDbqVoLQBgZ1Kjp7oPk5tQMqyRDfFnbxW0K63WpluJhnBvK7D1TdvgGTo2KKSUN4fCWX2c1I2e3VpFcantDK8uI3a1rYAHQfGv0NB216Q31qtzFROMiKkOg4j30+GAikqkYilSjwHBk1SKzMb6Pr9JyGD232s+g5ArYvkbkicyW8DbRXXrifFSeuqOAMJ0s60KAElQN1K1N5tgeQ4aSHNXSiOhuv5HDKocxif0sa43u0ozFOclgRJhfY7RL3cMNnrSHWvbtak08D4X2ze6yj1vd5TaZGruiVx2AycpO5+RoCVgiEnqJUZNjktYMAR6n//riNnRZzpPESMfh0MiPncDThCj9yWXeJfKrrXUeqDKrYNfQ6I/32+gcegtLUtiJ6Ie0DKTsHAo0Pp3BaKlrYtOGNcvA2+U8b0wFF5PyPAdWDUuerlhsno8/TI9RwYeYYe2WGQ0wKGn6ynBWNBOxhwuHq5YdF5iNwzUJ0StyzoOADa91UrN0wGTdLTsS2VhpKz4t1ytOQs9QYfSFRR1zkbFqPPU1+uB/L7GnmmerlhYadg1Nnq72eAtDaNc2ClTXfos7/6oIpti46Ks4Pb71BoEc4dyFC0tGVZFLcrYGI/xRczQ6J1fprjRxaRTmmo40ulYeBR8e3u0K1EXw2klYIxF6kvYwiLYSfre9PCdWTu4sqYC/QYfCBG3/BT9MgOg3GX6DH4QBzcgTGN9KXzYNQ5egw+OwW9JkKHpneA0UL7vtB7Pz1Z1FQaSs6Obzv0vY4OzeBrML4HYy/WIzsMRp2rtlNXTVxH9FRcGXtRaNUWoVmHjuvx7QPiGeU7c3xPPY/11WT8t/XKbywTr1Jf+hNgWdC6GwyOYcTFsmC/a8DTkEWDqvKpI6DjXnrkN4WCdjD6fD0GH4jRN+FKyG2pR35T6DYSeu6tr2zOc2Tdx5ERZ+i9zO46MOEKffKbwj6X6zvrQO4QxjWrse81+gIDwR2XHuP1yG8KOQWw92X6ApGptDg6cbxb1aG/lLmGlH0OzbpOp2wOHNiZoUWKLwU3kRa5KS7Zv6/ejpV2GsZfIgZUnGjfF4ZO1lsK4blwwPf1ZFSawsBjZDOrvmNQEzcD+39Pn/zGsve39Zff5LWSccSNQ36qLzAActYVjYI+B+obQ2NI5cB+16K81vtrY0hLRkXXHZHG0qqrlE3pCgyA/Nr2uzZ+jVd6TYQeijt11cbNwP4/0Ce/sYw+T32TmtrYaXGy48Z+14YaGAjVynFcj7+cNlJti9gm8qOjB9GuMFd9t6na5BTA0X/SO4aGYFlw4u360pIBdkpqISdcqXccDSG/DRx7s/pOGLVJ5cgdlzg1I+g8GPb/vv5yOcsWB7dljJpgDDtZ7uXodtI8B064RV/ZYGM44P/khVztAQ0bjv+H5jE0kOP+LmWuOrFtqdc/6Ed6x9EQ0vlwwm36shkBqRzJfg89Se84GkLbnnDYL1DahnpX2CnY91roMlTvOBpCnwNDLxEN9dRMV7W5veLgeNSRTujbgfMm9CYVBcfITsOIU+PTqWD8t6HnPnqjVAGWBYf+ND5lQEf+TtKpuo1lEGfnxNsgLwaZSDsFk+9Eu/IIsHPh8N/oHkX9KGgHx/xZT3e42thpaF0sezYOdCuRzJ92JwM5b/sfJncO4sCI06tKMKKgJ2yJ1MalZeshN4jBHIXugL4Hx/4VCjvqHkn9OOFWOZ91B5ADJt8Zjd/jnshtCZNvDz0IGvrJaVsWVx86gCHdom24tMhN8ZfTRuJ6miPyNfE8iVZFvYSqfV84/Fe6R1ELWzZzFIyB3THgcIkWROXQsVNQ2BmOiIHBPPG70nggCkYLyDhGng5jLtQ9kj1zxG+kjCAq+8NOSf10zwm6R7J7Urlw0l36M7c18X045k/RL6Fq1RUm/SUazm2A58nvM52neyS7p8fech8nCsEokHMjryVM+qvukeyZsRdJW94o6YkuQ+NxN+2IX0PLrqGvu6xoHd/3+evpJfovWO+Gn0waTJfW+dHIZgTYtpTVTLpJ90jqxk5XGfQROQADUlW13/tdq3skdVPQTiItukumamOnYMz5+vrc14cuw+DgH0fHUA7wfZj0Z8nuRZWhJ0XLuQ3wq4y+vAg/9nrQ9dLOOCpGC0iUNpUnZ0nU9kOAZcFx/4B0QbTGmEpDh35w8A26R1I3uS2rnNsIOWgg58eQ42H4qbpHUjft+sCRv41WYABkDxx4vZ6HF+tLv0PFScuCc5uVEyCdsunfuSW3nzMmkvc1vnNgP87eu1e0nIwAOw3DTqqqL4wYVlXWoPvYaCneAMuSdHMUD8K8VnDe09KmMCpRqpp4Lpx2LxRHsKygXW84dyqRKZmqiWUBFpzxcDQjzL33i6bRAlUlVEVw9uNSjx41xlwI+18XLUM5IJWGvgdFNyh1zJ9hwGER1RM27Hs1jPuW7pF8k3QenPWo3CeJWmAAJCN04u3RbObQsguc/2y0SqZqYgHnTonmG0xFo+D0+7MWBM3aCZqyLQ7aqxM3n1ESKWfjvAm9+OHRg3QPY8/sd61cNo0KliVKbdhJ0VS8/8MSZ2jQsboHUk1uIZz1mKRPo6g8QJyfVK4Y9FF6ZKhNd7jgeckGRdFoAZm7vNZw9hPRqmHuPk7WnWVHd8/aaeg+Hk5/MFrlLCNOh2MjasQHWDaMvTB6ZY+H/0qM+KiuuYBJf5EHGKNCKhdOu0/KCSOrJ6rOkrMelfKuqFDYUZyMVt0irCfS0h77/Ofk7k1U6DJU9H46L2tBUMv3s5tj8nyfNxat5YoHZ1Lh6I2qXX5QP/7vqBg4GTV592Z47ed6x2CnJYox/NRoRgpq43mAD09fDrMf0TuWgnZwzpNyoTSKmYzauA445fDASbD8Q71j6dAfLngOWnSKrvKoievAlpVw3/Gw8Uu9Y+m1L5zzhJTYxGHdeS4smwYPnQ6V2/SOZdy35A4EVjzOO4Dp/4Hnv6c3c2VZksmIYqZgV/i+jPmFH8BHd+kdS26hZEV77xef/eo58PCZ8MXresfSprsY7216xEdP7FwP9xwL6z7VO5bu4yTLklOQVec2644GgOv5zFu5mWseLWXJuu3ZFvcNWuWl+dlxQzh1bA/lskNh/lR4/lrYsUG97DY9YPId0s876hGqmgRK5N2/wls3glOhfgxFo+Hkf0O7iHQOqS+BEnnpR2LA6GDQsdICNbdVPJRHgOuIofzYubDkv+rl2ynpkHTQ9bIH4mC0BHgOrPsMnvwWrJmnXn5OCzjs53JJPW74Hnz+BjxzBWxdrV5+yy7SyGTAEfFxzmry0V3w6s8hs0O97M6DRU90Ghg/PeH78PovYNqtepzcfodIBUNB+/jpicx2eOYqWPC0njGMuQCO+oPMW5bXnRJHA+SNDc+HP7y0iLvfW4KqZk8HDOjIn08dSYeWedG8k1EfPAfKt8CzV8PCZ9XJHXMhHPU7sHP0995vLL4H67+Apy6FsplqZKZyxdDb7xqRHyflERA4akv+C1Mvh83L1cgN2rAOP0UyUzofNGwsnitz9/Yf4L2/QWanGrltesAp/5YoVZyCAjVxq94MePv3EiRQ9YZAzwlyl6V1cbycs5q4Djg7JbMx51F1coefKmW1uS3iedaB7NktZTDl27D0PTUygzcWDrpe/v84Gco18X1YOUPmbv3namTmtYIjfitNTDw3nnvW9+Scnj8Fnr9OXSC5TXdpJNH3oGo9n2WUORoBvu8za9kmrnuslC/XZy960DIvzQ2TBnPG+J64nh9fJyMgMLrmPQkvfD+7i7JNd3koqO+ByhZiVnEdmbt3b5bshluZPVlFo2DyXdCxf3yNvZq4GfAy8NKPYcbd2ZU1aJJERfPbxNdgqYnvwba18OrPYO5j2euEYqel1vzIG+VSdVwNlpr4HqyZLwGCtQuyJyenAA79mWQx4hoUqElgvHz6EjxzNWxbkz1ZLTvDcX+DgcfENyhQE8+R3/+Hd8Brv8xudqPTIHFsuw5PiJ5wwHfh9V/BB7dlN7vR92B566GwczwdjNp4DpRvrQokP5NdWaPPg6N+LyW1CvWEckcDJLsBMHXWSu56ZzGfrgmvJrdjy1zOndCbCyb2pmVeOv4ORm08R8qAPv6XHIhbysL7dod+8sJ2ydnSvjYJBktNAsNv2i0w416o2BLet3uMl3ceBh2TDIOlJoGz+dUiiTLPe1IckDCwLNjraGl+0GN8MgyWmgSG36o58MpPwi2nSudLNPnAH0LbHsmbO9eR9TF/Kkz7B5TNCu/bBe2kleOEK+SCZhIMlpq4jgQIZtwDH9wOm5aG9+22PWGf70jGO5WTrLMOJEJevhk+uBU+/jfs3Bjet7uNhIlXw9DJcq4mTsf6cj/tvZvlfqRTHt63+x0K+35XAqBxzWLURXB2r/hYAqKfvBCes5bKkRbn+10rZXoagsdaHI0Ax/VIp2xmLN3Iox8v56X5q9iys+Gp8ry0zcR+HTltbHcOG9IF27KS52DUxnMACz57BWY9IBeyGlOikdcKBh4No86DPvuLgkra4VcT3wd8cdbmPQWzH4JlHzSuRKNlZ9nAo8+Tzg1uJr4lZvUhONx3bIDSB6U8Y828xkXq2/eVzj6jz5MWp0E0MakEP9/GL2HW/bDgmcZdBEzni0PW/3CZu/w2gJ+MqGhdBPuqrFTmbv4U2LG+4d9J5cgl+ZKzYcgJ8vtIkrGyKzxH1sbit0RPfPoSVDbinmROC3nhe9Q5UheftGDKrgjuqi18Vs67L99pXIClRXsYciKMOlfahyddT/geYEHFVpjzCMx+WPZuYwzntj0lmDL6PGlznnT7JNATW1bBrPtgzmONK0ezLOg8FEaeIdnuFh20OmdaHY0A1/OxLbAsi5UbdzJr+UbmrdzM3JWbWbp+B+UZl0pHnJL8tE3n1vkMK27D8OI2jOrZlr6dCknb9v8cl2ZFsPE8FzZ8IR5xWSmsKpVsh1NedbDlipHSrpd0QCoqkVrudr3lO0k39HZFcOC7lbBmAaycXjV3s8WQcXbKvKbzRNF2HFA1d6PkLZGWXaoPzyQbersiWHeV22W+Vs6QNbd6nkQDnXJxQHLy5QGqzkNkzRWNFmWb36Z5rjnfrzLSUrBpuRgvG5fAhiXihGxcIgGDVK6sucJO0KoLFI+R3vXFY6rWbMIV7q7wPOlFb9lytq2YLmuurFSUsbNTAgh2Ws66lp0lgtytRPZrp0FVc5dwQ29XBHvN92StrfhY5m7VbFmHzs7qeUkXSPlsUUnV3I2D9n1k3pvjuvufnsjAV5/U0BOlkiF3ymV+03kydx36fV3Hti6SeY9bc4YwCOYusxNWz62eu9VzoXyT/Hvfl7nLLZSIe7eSKj0xqtpAtuz4l3A3lGDPlm+WbO7KmbLm1syXhiNBg5t0PuS3lkdti0bJ3BWVyHxGZL9GwtGojeN5WOw+K+H5Pq7nk9PcHIs94Wak7Gl3JRRBt4gILMBI4VbKxffdHWhBOUdzUxi7w/elTCOVu/s/52Zk3pqbU7Y7XAfw92z4uk7V3DUzZbs7PBfwd++s+p78uebmWOyJ+pxjRk/smvqcY0GGvLkFUnZHQ/TEnmyY5kZ9z7H62DAaiKSjYTAYDAaDwWAwGOKNcRkNBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMISOcTQMBoPBYDAYDAZD6BhHw2AwGAwGg8FgMIROWvcAdovngO8BFlgW+D7gy/+fytE8uIjjZvjfXH1t7mxIRfvXrh23suq/1Jo7KwV2SufIoo3vg5cBLPnHonru7DRYJq5RJ54HvkP1mgP5Pz7YOfLvDLvG6InG4zqAmbsG87+zjupz7X96Ig22OevqZHd6wszd7vE9Oe9ipieiY3F6rvxnYMhtXAorPoaNSyCzU4w/Ow3pfGjVFYrHQqeBchj6nijq5mpAu45sTsuWRbjuM1gxHbasBKdC5i6dJ3PXtid0Hwft+1T9+Vrz3txwM9UK1amA1fOgbCbsWAeZcvBdSOVBbgvoMAC6j4XWRfLnPQewm+/BWHPuKrZCWSmsng3lm2UufV/WXV4r6DwUikdDi/bf/LvNDd+XtRP8/NvXwcqZsHYBVG4Fp1IURjoPCtpBtxLoNhJyC+XPN+u582T+gvNq8wrRE+u/qNITFVV6Ig8KO0PxGOgyBFK535z35kbN88rzYMPnoic2L5ezzq2UecrJhzbdq/REf/nzvif/2EbH4lRUn3U7N0HldtETVgryW0PnIdB1BLQplr/rubKfm2ugpeZ5VbkDVs2GVaWwcyM45VV6IhdyW8rcFY+Bwo5Vf9eRvR5RAzrr1Jy7nRtFT6yZJ/rWqZB/n5MPea1FRxSNEn1b++9qRv+p4TlyeJXNggVTZSJXz4WKLXv+u6lcWZhFJTDgCNjryKrNbCV/Yfq+HPyWBV+8AZ++BKtmwZr51Qtwd+QWQtfhsjAHHw+9Jlb/LpKOVzVvbiXMnwqL35SDb92n1Y7X7mjRXoy/4tEw/FToNChSmzqrBGtk5yaY8ygsmyZKd+OS+v391kUydz32hpFnSNDAdZpHkCD4ObeshNmPwIqPZO62rdnz37UsaN9X5q73frLu8lo1nz0bzN2a+TDvSdETq2ZD+aY9/107LXu0qAT6HQqDj5V/59M8ggSBofvle7DoOdG1a+aJY7YncgqgyzCZu4GToO+BX3f0koznSbTdzcCi52Hpe+LUrplf5bTtgdyW0Hkw9JwAI0+XeWwuZ53nyhqp2AZzH4cv3xEdu2FxVfZiD7TsIoZzj/Ew4gxo26P56NhgjWxdLTp2+YeiJ7asrN/fb9db9ESvCTDidAlWadYTlu/X57eeBTwP3HKYfjdM/w+s/7zp32zRAUaeCROvhFbdqgzxhCmS4Gfavg4+uA1mPVA/Q2VPtOsNYy6AcZdI9D5p8wbVm239F/D+38Vgqdja9O92K4Fx3xLD2bKTqYSDQ37ZBzDtFnFs3UzTvmnZ0OdA2Ocy2Ouo5Cphz5GfdeFz8NGdYrA09dhN58GgY2HCleLwJnbuXJm/WQ/A9H+LkddU8tvC8FNg3+9KhjcwipJEsL7KN8uam3mfZH+aSutiGH0u7H2ZzCMkL6gXrIdNy0VPzHmsfg7tnug0SPTEqHMkSJq0NQfV51BZKUz7Byx8tn6Bzz3RayKM/zYMOT65mbVg7j59BT68HRa/VVUS2gRSOTDgSNETvSZo0xPqHQ3XkSjBO3+BabfKQRg2dloUyVG/l6hfUhal60iq8ZUboPTBGncJQiS3JYy/BA66XlLBSTFePBe2roIXvi9GcjaWfcvOcMD/yfwlyXjxfSkne+EHsHJGdmR0HABH/FaykkkJEAQ/x/yp8Nov6p/1aSg9J8Axf5YSoSTMG1Q7Z9NuhXf/CjvWhy/DsiWbe8wfJUiVJD3hZeD1X8GMu+uXuWgo6XwJTB32c6kNT0qkOdATr/5MKizqk+FuKPlt4ZAbYNzFyTKafQ/WLhIdu/S97Mho1xsO/TkMO0mC1UnISAZ64vPX4OUfw1efZEdO0Wg564rHKg8OqHU0fA/WLoSnLpXUbbZp0QEm/QWGTo638RKM/dNX4Nmr5SDMNh36w0l3yeKMc8QqMPg/ukuMvcrt2ZfZez+YfKdk1eLsbAQXRV//lRh8TY2u1IeRZ4jRnC6It5PrOZIte/a7sODp7Muz07D/dXDgD6UkKNZz58KmpfDUJXKHINvkt4EjfyeR5jgHCHy/ukRq6ndkDrNN255wwm3QZ/9q+XEkMFpn3Q8vXR9OpntP9Bgvc9ehX3xtE6gOHv/3TxJAbmqmuz4MPg6O+wfkxzyQHASPX/wBlD6UfXmWDft8R5w1hYFkNY5GUM/49h9lIdanvjFMhpwAx/0NclvFTwEHC/GF78Psh9XKtmyYcAUc+jNi2a0qiE5NuUxqRFWSWwiH/QLGXxrPyEuQxZjybWkuoJJWXUWJ7HWEWrlhMn8qPH9ddiLxu6PLMAkQdB4cP+MluEvw/i3w5m/l3FPJgMPhhFvjmd0Ishiv3CAlZioLFSwLxlwER/62KrsRs7kLggJTL4dPXlAr+//ZO+s4u4qzAT/n3HtXko37xoVAfGNAcHcL7lIKRdtC3fvVW0qpUKxAcXcv7hIiG4VAjMjG3Xb3Hvn+ePeyS9hNNrvnzsw5mef3o0Wy952dOzOvzjupjJTwHfQTwImfkxsGEoF/4lJYNk2t7Bbt4ZjrJbsRVyd3zqvwzNWwoUKt3A4DJBjafbSSecu/o+F7kN0M952sJjrVEC07wXlPQec94qNEfA/WL4S7j4+mvrapdBkC5z8jKd+4KJHAh/lvw8PnQvUmfeMYeBScfo+suTgpkY9ug5d+pCaL0RD7XA1H/C4+SiQ3V89/T+6d6cJNS2Cl7Jx4zBuIsedn4cEzpTZZF0Vt4ZxHpLwgLvvV92DzCtETUdx1bCrt+8EFz0JJ13jpicUfi57YvFLfOPofAmfeX3N3IyZzF4ZyWfmZq9RkMRpi1AVw/N/l7+MQXMnps1d/LWWhunBcCQ7sfUX+ReXV0fA9uUR193FSMqWbwtZw7mPxUCKBJ/WO95ygPipaH+36woXPxUOJhIF0CXnsG/m5x7Kz9N4XznlMLvCavu5AMo9v/F73KIRR54vRbHonuVz7zyculSYDunEcufMy7krdI9kxgSdtL+89KX93gHaGTDGccT/0O8j8/ZoLRt11XOO70uSTVt1ET7TtY76eCHzJdD94Zn7usews3crggmckGx4HZ2PCbfDiD9Vmzxpi8Elw6h1iPJvubIQBPHet3J8ygQN/BAf/NK8i8udoBJ60NrvjcGkbagoFJXIQdh1m7mb2PVgzF+48Unonm0LbXvDN16C4vblKJPBhzivw0DnqS/S2R+99JKPmZswuo3rnermTYRKjLoAT/ql7FA2Te2zq8W+a4WTU5ejrpDmBqU5a4EuJ1F3HSttVU0gXwjmPS6cWk/XExqVwx2HSCtMUSjqLnmjVzdxL4oEHCz+C+8ZH0xUpKrqNgAufF2fX1HUXBtIt9PlrdY/kqwwZD6feifFBqeeu0Zvxro+DfwYH/jBvH58fiycM5RC89ySznAyQMpp7x8uDgL5BhmiOwIPNyyUNbpKTAbBuoWSnvK16S2oawvckDf7I+WY5GQBfvA8PnyOX5kwkCGDSXeY5GQCT75Y0s6k4jnTkMs3JAHjphzKufHTPiYQQHjjdLCcDxPh88Axpp2uknvDlram7jzPLyQDYtEIcx8r1Zq47PwsrP5NMhklOBsjbMPefZm5UPvBh5tPwwvd0j+TrzHxSMgUmOxmv/dY8JwOkguHj2/Nm1+VnNTsOvPIL85RHjq1r4bGLzFyQbhoevySatzHywcrZUoNu2kEYhvIq8GPfME955Pj8FeneZJryDTxYO0/S4Kby7g0w7y29tcD14XvyPsbHt+seSf2EoXS+2rTMvHUXBvDWn2HBu7pHUj/Vm0VPhIbNG0hJ19NXwtoFukdSP+sWSucrE0vPAh8eOqtxjwLrYOEH8PZfzQvmBb7YJc9ebUa5VH1M+q90+TMt0Ohn5Zx793rdI2mYl34Cq+bkZe6itxZ9T6K3H/8n8o+OlKVT4R3DNnPgw0e35K8HdVRMe1jeojBpMzsOvPgjM+qUt8frv5O2k0bNnSutRE110HI8faXcuTFFyQWBZEif+67ukWyf6k3Sec0ko8/3YMUseOdvukeyfVbPNS+bFnjyiJzqDkk7y+cvS8tOk866MITX/s9cBy3HW3+GZdPNyqa5KenMpaL1b3N4/loZoym2XRjKHnjqCnN0V3341fDkpeSj7CJaR+PLCb3c7AnN8fZ1EqE3YTMHnhjJr/6f7pE0jme+LRc4AwM2s+/BnNekB7rpeJVyYdiUjFAYwHv/gCWTdY9kx6xfJA8amZKJdF1Rajq71TSW+W9L1sWkrMYTl5plhDbER7fAogmG6Akftq4zO/tYl5d+DFvWmLHufA+Wlsv3aTqBJ9m0wDPDlgp8uZcx7w3dI9kxm1fBs981R8c6DvzvZ2retWkuFVOkeiBiJy3ab8Jx4JWfmx8tyOFna4w+AwwXNy1vFmS36B5J49i0XOo0TbjY7FdJi724sPhjM0qoAg/WzIM3/6h3HDvDpLtqSqg0G325kikT72U0xCu/lMvDuoMDYQBv/1nuP8SBMJCz2YQSKjclZ51p9/caonK9ZCJNyaY9dbk5ke4dsXouvPE75AVOjYSBtE9+5Rd6x7EzzHpK3jLSHcjwPSmZmmTgvYyGeOvP8nZWhHMXnZUYhvLoiIkXXbbHsmkw/TG9td++J69+f/G+vjE0hWmPSEZIp8Ec+PDhTeofvGkub18nDpJO3LRc/ja9ZGpbXv2V/q5nqbSMI05UbxanUndgpXIDvP8vvWPYWdbMk/pvnYZL4MlbVLNf1DeGpvD5y7DwQ71z53sw8XYz2uzvDB/fIftFKw68+SfzS6a25bX/05/VSKUlwGNCVqqx+NViF0TY9SzCbyGUEgzdUdqm8MG/9LbhS6Xh/X/ok98c3r1Bb7QqDKSfd9yoXC+paF3KNwzkEchPntUjvzlUTBGnXNfc+Vkx9nQ+jtZUpj0CW9fok5+7h2bCuwU7y4c36zVc3LTeB76aw3t/19uu1XXho1v1yW8q2S1in+i0qyrXwdSH9MlvKmvmyXtauoLIuRbKJrwNtLPMfkE6s0aU/Y7u1PSzUH5/ZB+nlGXTpQZXx2YOA2kBbGrnlR0x43ExmnXgZ6Wl3aYVeuQ3l49v16d8w1AMvriUEWzLhzfrm7tUJp5GC0i06uPb9UaX45b1zrF2gUTndZXtbVxm/gXwhvjsJX1Z56DmDt+aeXrkN5fJ96CtfCrwZL96lXrkN5ePbtUXRHbT8NHNemQ3lzAQ+yCi5Hc0jkbO4Itbaq0uk+/WE60KgUl3q5cbFX41TH1QT9QglYEp96mXGxVr5knEQ1e0atrDeuRGwWcvyqVYHWxcBvPf0iM7Csof1OOk5Qw+U1t3N4bJ9+op2ws8aXYR18BAGIrBrMPBddPxDYKCBNJ0RebjPndfvAvrl+gpXarcEN/AAMD0RyI7b6KxrFMZmPFYJB+ljVlP6znEXTf+czf9MT1Rgy1rYMHb6uVGybSH1Tu4QSAZtLhmgqAmuPGEeuXrZ6X8KK4GH8Da+dJlTLXyddMw41G1MqPm8/9Jtz3VuOl4NR6oj+mP6nFws1vid69lWz57Sc/cLZ0ml9LjShiKjlXt4PpZsSnjdv+xLptXSeOVCAKh0Vk4iydG9lFaqNqop+Z64zLzXnbdWZZNU7+RAx8WT4jXJav6WDJR/eXc0IdFH6mVmQ8WT1SvfFMZ6RoWdxZ9BIGGCGnc9YSfhWVT1Z871Vtg5adqZUbN6jnqqx7CEBZPim/pT45FE9TrCT8Liz5UKzMfLJmoPhDqppOhJyKysaJxNDZUxKfd3vZY/LHaCGngx1/xgnjtKz9TKzMMzX15fmdYMUt9VD6VkX7ycWdpuZ4OSkmZu1SBWplVm+JbJ1+XJZPUOmlhqMe5yQcV5Wp/j6DGMYw7q+eovwvppuX7ijs6fgfHSYaeqCiPpFS0+Y5G4CXDc4Maw0VhB6UwhKUJMJZBogYqDeZUQg5BP6snUpmEuVv5qfrUdOUGWLdQrcx8oPr7D8NkKF6oUb4KnbQgG8/ONfVRMVlt9jtVAMtmqJOXTxZ+qPY+X1KM5Q1L1AfC/awEEeNORN9/NBmNJBgtIL+HygfokmIsQ83cKW5zm4RDECSrpdJJ27pWDt+4E/hqD/MwEEMpCaz6DLIKy0kCLx6vzzcG1edOqiBZekJ1GcvyhDgaiyagtPuUVxX/cr0cSyarvVe38lO9b7NFxcZlsHl1sz+m+Va1m07OYtTxe6xSXHKUL1Z+ovZSc9Wm+N9tybHyE7VOWtwerdoey2aoO9ADLxlRKhClq/JOWiqTHD2xeo76O2krZ6uVly9Ur4EgSM7cbVqm9k7a2gXxfBetPlbMUrdnfS85zi3AipnN/ohoVm315kg+RjtZDd1Eqjepl5kPVHdi0fFd5YvqLYqdtBi3od6W7GaURvmSctaB+t8lKXMXhuBVQ4FCoy8pekL1ue1XSgt2XbgpSBVKVipdIH+f+/+v/btt/nu6UBz03L/rMkzt2JOkJ5SePUFyzjqQdRCGzboPGc1J6ce4hVdddHjvnsZDMEpUH+Y6lUfUBNlmb+RGEwbxbrm3LYGn0M9wkjV3yvdsguZOebvMhJx3qrs/qdLpv9b0aG0+iXunrrqo3q9J0hOBL3ZDM+4vRxSS0dD5JV8Egdp7Gjq65iSCBM2bKicjR5LWnNJOPIq/p7yjuouRnbumk5C50/Eorgrmvg79D6n9Z79ajM36/r+x/27bs619Xxh0vLrfSWVjnHwThijbQyHJ0hNhQHPnLhpHI1McycdoJ5VR62QApIuSkaLMFCVbXj5RuX8cNzn7FWT/qDzT0wmau0wLxfISNHepQrXyknLepRX/Hqrk3Ts+/zIGn6jW0UjKmoOa30VRcMBxEqYnipttF0djVRe3i+RjtFPUVr3MxMyd4t+joCQ50bGitmpTuy06qpOVb4rbKlwHTnL2K6j9XcIgOXOXLpKaeZUkZe6K26qVl8qod27yRXE7tRnc4vbqZOUblbad4yZnvwK06NDsj2i+hvaz0FXxJaV80W24eplJmjuVxnK6EDoMUCcvn6hed50HqW9FnC+6j1bXiSWVgdKRamTlm0wLaNdXnbzAT85Z12WI2iBHGEBXDbopH3QdobbNKEDnwWrl5Yuuw9U+FNm2pwT0kkDpKHVv37gp6D5Kjax847hy3jWTaNrbliZkUruNVGss+9nkGC7dylBeR1xaplZevugxVm3bwnQhdByoTl6+KCiBdn3UyuwyWO13lS+6DlX8ZlAGuo9RJy+flI5UaywHfs35mgBKy9Q2XQmD5Bh93UerfSjScZMRHHBTct6ppG0vKGytVmY+6DAgkpLX5msax0mOsVxahlJj2U0nZ+56jFUbJferk6F8C1vLoaSaJMxd12Hqy+dSBdBpD7Uy80G3Mml8oZJOu6s1lPJFtzK1xnIqAz0S4qR1H632wb4gEJlxx01LkEMlgZ+MYF7HgRJcU023EeplRk1E3380WrqoNbTtHclHaaXHGLXGsuMkw+ArbgdtuquV6WaSoUB0HEZ+dTIUiOroKEiNdFLmLlQ8d6lMMspYeoxR/7p1x930GEtRksqod9JTaeg1Tq3MfNBpD/VOehgmwz7R8TskxUnrVhZJa+1oHI0whD77RfJR2mjbG1p1Uy+3sCT+nm/vfdXLzGXS4l5D2mc/9T2+3Qz0O0itzHzQ5wDF7W2R76rPAWplRo3jQN+D1BvLQRB/PdGivZ6yQzcNPfdWLzdKeuypfs2BtIWN4EKrVvrsp/5uSyoteiLurVr77C9l6koJ468nQL5/t/l7NiJHI4CR50XyUdooO0u9wQeyAUacqV5ulJSdA76GuUsVSMu/uOI4sm9U9yt3HImQxfmCaYsOMPAIUYYqSWVkzcXZwe21j/oMZI5RMdcTw07XY3j5WSg7W73cKCk7W4PBh9gnw09XLzdKdO2bVl3FUI8rmRYwdLx6B9dNw4BDoaSzWrlR0mVITeOL5p930Tgabgp6j4P2/SL5OOU4Low8X88DNakMjDg7vmnxlp30GHwgCmT0herlRkWf/cXg02W4jDxXvdyoGHGmvvbG6UIYerIe2VEw8jw9Bp/rioMb53tpus6bVAaGjI/vBdPCVjD0FD0ZjRAYc7F6uVHRdRh0GarnvPOzYhvFlSEnqX8v6EscGB7jIPLIcyMLIEe3cn0P9vpWZB+nlD2O1WfwARS1kUhZHNnzErS9WuumoOee8TVcxl2pJxMEovBHna/n7Zjm4qZg7yv1yQ8D2OdqffKbQ0kXGHaqHoMPxHDZ+wo9sptLn/2lNbQuBzeVgdEX6JHdXEaepy+Y5rpyx6XfwXrkN5e9r9QTGABZc0PHQ+tSPfKbyz5Xq7/Hl8Nx5KyLY5fCwtYw6sLIAsjRnZiptAwsjg+V7HeNnrKpHGEoY4jbA3SZFrDXZXrfZPCzsM939MlvKh0HwsCj9GSCcqQKYMw39MlvKoNOrAkMaNovbkq+v90O1yO/Oez1Lb0116mMRLbb9NQ3hqay33f1BQYAcMRwilvnLjcN+3wbbQEpkO9t/+/pk99UWpfCsNP0BQZy7HW5XvlNod/B0nxCl33iONC6m2Qi48boCyN9GT5aTZ1Kw5F/iPQj807Z2Wof/aoP15Wys7hlhA79hf5a9VzEpW+MLl45Dhz3d81GC2KoH/iDeBl9ha3gqD/oi1LlCDw4+rp4vTrccTcxVHVH2MIQjv2r3jHsLLsfAwMO0xsYcBwpVT3g+/rG0BT2vxZaddHs4Kah7/4w8Eh9Y2gKxxiwT9w07H25tKeOC+lCOPZ6vQFkED115O/jVfLYujsc9GOiDAxE62i4aTHcdzsi0o/NG626ibGguptDfTgOHPZ/8bnn0muc/mxGjsCH8bfod3oay5iLoc++eo0WkDXnFsCJ/9Y7jp3hiN9By876152blvdPDvm53nE0FseF8bcS9ZHfJFJpyebF5YJucTs48Ub9zi3I97j/9+PTqbDLUDjwR2Zk6wMfTrpZOofFgaGnSFm3bj0BgAPjb9N/7jaWg34K7frqD6q4KWjRUYJjceHEGyFVGGlgIPrdH/hw0k3xqP0+8UaJSJpwCIJcRh9/qznjaYhMCzj5NjMcNJDNXNIVDv+N7pHsmLa9xVhW3Za1IVJp6HcgjL5I90h2TP9DJKVrirJzU3LPpueeukeyY8ZdBaWjDDFakLPjmOvlzojpHH2d3KMzZd2FoZy/ustpdoSbrtETugdSg5uS7/HYG3SPZMe07ATH3aD+Uc2GSKWh2/CaEjjD6TEG9v22VIqYgJuSO0oDDtM9kh0z6nzRsxHriei/CTclEaCj/hT5R0dK2Tn6U+HbkkrLC9t7XaZ7JNvn0F9Kek13tKAubgrGXmz2+xCOIxE1N2NWb/IwlIiLjhfKG0thKwlgmBBVrksYwPj/QKZY90gapuNuUuZo0ppzXAlYnPBP3SPZPnscC8NPM+usS6XljtABP9Q9ku2z//fk8rxJOtZNSyeioafoHsn2Oe4GyLQ0x1gG2bMH/0z9o4s7Q7pQMi+mBEFzfBmAb6N7JA3TpofY7XkIguZnFbtpeZfCVIO5555w7N/MW4wgxsDhvxWv0kTKzpZ6TVOie3UJfDj1LujQX/dI6ueI35lRMrUtuRKqsx428yBMZeD0e8womdoWNw1te5pbVtCyI5z9CEaUTG1LroTqwB/pHkn9dBkqGWZTosp1cVy5q2HqO0J7HCd13iZm54MAjv+nfL8mcsAPYNDx5ukJABw4+2HJuJiG48o53K6PWYEBqC2hOv1eM5s5FLYW/R9xyVSO/J4CR/9ZMgcm0XUYnPukGC8mHoIgX/SZD0LPvXSP5KsMOl7q+U0p+9kWNyWR7wufN++C84E/lPIVU0mlodNAOPdxKGipezS1uCk45Q7oe6CZhjzIuAYdB8f/y6ysQVEbOO9paNPLUKOlhoN/al7L2w794YJnJVNlUlR5W065Ux4GM4l+B8Npd5lTMrUtrisddS54TrJ9JrHnpWbf+0qloXUP2RsmdRh1HDj+7zD4BLP1RJ/94NT/muUIZVrAuY9B5z3ypifye4KGodyDMKWFZo8xcOELci/D1MUIMrZUBs5/WgwsExh2Kpx2tygPk4ypbUmloUUnuPhlczIbh/xCUs6m46aljv/8Z8y4Y5UqkAjQoOPN3q8gQYuys+GkW80Ya8uOcNGLeVUekXLUH6XFtwl0HgwXvyJRPpMMgm1xXDmLz3pYumKZwMCj4JxHZGwmO2huWoJS3/ifBB9NYJ+r4ZjrdI9ix6TS4qBd9KIZL1+7KTjxpppHlw1ecyBj3eMYOOM+Mx5pLmojdmb3MXk965wwVBSefvNP8PZf9NVY73GsREZTGbOVR10CX8q7nr4Spj2sZwyOI5H4w38DOGY7GXXxPajaAA+dBQs/1DOGTLHUPMbt9fLAg1Wfw0Nnw5p5esbQshOceif03tcMw72xhAF8/go8+S3YulbPGDrtLsZnm57xcDLq8uFN8MqvwK/WI7/fwVKmV9AiPnoiDCSo9+IP4ePb9Y1j9EXSUtRxzDf4cvge+FVy1s17U88YUgVy7zFuj4D6HmxYAg+eASs+0TOG4nbiZOx+VHzWHIhtt/BDeOxC2LRCzxja9YUz7xd9keezTp2jEQawdBo8eSmsnK1EJFBzMf3PMOIM+XLjZLSA1JO6Lnz6PDz7Hdi8Up3sdn1h/M3SyjaOBJ4cPh/eDK//FrJb1cnuuZd0XGnTM35rDkSJhD688kuYcKvacrmhp8gdqsKS+Bh7dQk8qFwPT18Fs19QJ9dNSVeYg38GOPFzMkD0xKrP4YlLYWm5OrkFJXDEbyX7Hkc9EYZi4M99A565CtYvVie7dSmccKOUcOXGESdy3/fHd8Crv4KqjepkdxsBJ/9HMgRxMpRz5N6CevOP8N4NagPJA4+SipnidvHUE74H2S3w/LUw/VF1ch0Hxl4id4HdtBI9oc7RgJpFGcIbv4f3/5n/RTnwKLlTUNw2nguxLoEH1ZvhuWtgxuP5laVhIeaVwIf1i8R4WfRRfmWli6TGdtyVYjTFfd0BLPwAnrwc1s7Pr5yWHeUhw0HH1zrYcSVnvEx9GF76Uf6zG512l4vL3UbE02CpS+ABDrz7N3jrL/nPbvQ9UAIqJV3j52BsSy5C/9KPYfI9+Zc38lw4+i9yiTQJemLzCqkgmPNafmW5abnMf8APpBw57nMXBrBsuujYlZ/mV1ZRW6kUKDsrnkGButQNJD/33fxnN9r1kc6XvfdRGhRQ62jkCANYPlPKqWa/EH33p9KRsN+1cjEo7guxLrlF+fmr8M510ZcEOY60/D3gR9BzbDyjUw2Ry25MuU+c3FWfR/v5qQK5x3Lgj6VNXFLWHIjxEnjwwY2S3Yj6MCxsLeVl+38vvlmMhgg82LoO3r4OptwrwYIoad1duvvtfRngxt9gqUsYwOq5Ei2d9XT0r/x2Hgz7fje+2e6GyJ3bC96TcuV8lAT1PUDa6/bdP2F6omYdTH9MHN3lM6P9fDcFg0+STmtxzWI0RC6QPOFWqSKIOqtW0FKaCx34w/hmMRoiF0h+528w6b+SEY+Slp2k0cA+V9cEj9W+waPH0QCZWDcNa7+QutJpD8Om5U3/vEwLiYSOuUhKffys+Q8aNRXfE4OiYoqke2c91bx0b4sOMOw0GPtNOfxy300Syc3dZy/D5Lvh8//JWmkq7frAiLPkDY+WnZJlsGxL4IlRMf0xMZoXvt+8kqpuZTDyHCg7V7rAxOkO0M4QhkAoimTy3VD+QPMMGMcVQ2/kefImACR3v+b204alMPF2mPpQ8wyYdKFcnB59oby5syvoieWzYOIdsm8r1zX984rawtCT5azrMrT285NIbl3Mfxsm3SURZ6+y6Z/XulT0xJiLoU335OsJHAkOTLlXHN3mBJM7DxIHY9QFEoiCZDloOcJQ/vIrYcr9UH6/2HjNofc+ol+HnyYPQmtac/ocjRy5i2yOA4smwJKJUFEu9bmr5zRsyLQuFUOltEw65fTZV5yNJG/gbQl82XB+tUSvKibLvFWUS6lQfTiO3L0oHSnz130U9Ny7tkwliRu4PnJKsnK9KJOKclg6Rf5/y+r6f8ZNy2NFpWVSotJjT/n7JDtm9ZFTwhuWwoJ3atfcsmkNO7zpIug6tHbP9tpHuoIl2Vipj9zvu3K2ZCRzc7diJnhV9f9MURtZb7m567O/dHtJspG8LWEousJxRPku/rhWT6z6rOEy3JLOtfPWbaRE4Atb7Vp7NgjAQeboi/dFT+Tmbu2Chn+uXZ+v6tje42TOwnAX0rE166R6M8x9HZZOlXNu6TTYuLT+n3Fc6DBAdGvpyFo9EYa1ncJ2BXLn06YVoidya27p1IYj9qkCcWJzOrbX3qJzdzk9UTN3a+bJns3pieUzGr5rWlAir7fn9mzv/cSpNUBP6Hc06hKGEGRrHzTJboF1i+X/vSqZrEwLUR4t2suf8bM1ntouYiA3RBiIIsktqK3rYOMy8LaCVy2RvEyxlPXk3knws3KI7ioHX0MEPhDWGh6blsOWNbLufE+i7ZmW8jBbuvDr63RXxvdqHvyrMTzWLxYlkt0qazLTQqJQuUvx267TXRk/K3PiuGLQrFsoBk12a83L2cUSRW7TXf78tut0V2Xb/edVydxlt0C2skZPFEuGsWVH+TO5KOuuYiA3xLb7r2ojbKio0bHVkC6QPduqGxS1lj9Td53uymy7/7aulbnzqmSOcnqiTanMIUgQ0OqJr++/DRVio2S31OiJYrFL2vaqcWatnviSr+iJmvumVZtq9AQ1Ora16IncnwlDoxwzsxwNi8VisVgsFovFkgh28RCFxWKxWCwWi8ViyQfW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjnW0bBYLBaLxWKxWCyRYx0Ni8VisVgsFovFEjlp3QP4CmEIQRZSBfLP2S2wbjFkN4NXDak0ZFpASWdo0UH+jJ8FJwXuLu4zhQEEPqQy8s9b18HGpZDdCn41pAtl7lp3h8IS+TN+Ftw0OI62YRtB4AOhzAXApuWwebWsv8CXuStoCW17yd9vu053ZXxP1o+bkjW4fglUrpd1RwiZYigokbnL/Zm663RXxs/KnDguBB6sWwhVm8CrBByZu+J20Ka7/PnAl7WXMuvYVs7X9EQlrF8E1ZvBq5L5SRdDSSdo2Un+TN11uiuz7f6r3AAbKuSs86tlTjMtoHUpFLWWP1N3ne7C+EFIGIakUzIPqzdVsWpTFZXZAC8IKEynKM6k6N6umKKMrLOsH5B2HZxdXsd6QJ39t34JVK6D6i1AWKNjS6Btb9m/Vk/U8hU94YueqN5Uo2OR/VrUGtr0qP0zhukJ/SMJA5kUgMUfy19Ly6GiHNbMrf1v29KqG5SWQbcyKB0FffeTCQ/8XUeZBL4sLL8aFrwLFZNl3paWw/rF9f+M40C7vjXzVgbdR0PPvWsdtV1FmfiebMTK9TDvrdo1t7Qctqyu/2fcFHTaQ+au2wjouZfMYeDVOim7An5WFMCGCljwTu28LZ0mB2B9pIugyxAoHSnz13sf6NC/9nvYVcj9vitnw8IPaudu+UzZx/VR1Aa6Dq897/oeIMGW3PewKxCGoiscB5ZM/qqeWPWZ/Lf6aNmpjp4YKXNX2GrX2rOBL/MWePDFB7BkUu3crfui4Z9r27t27rqPkj3rpuW72EV0rBcEpF2XzVUeH8xdzbQl65i+eAMzlqxn5aaqen/GdaBfpxKGdW/DsO5tGNmrLSN6tpVpc9h1nI7c+bRpBcx/u3bNLZsmerc+UgWiJ3I6tvc40bm7nJ6ombs18+CL92r1xLIZNYGoeigoga7Davdsn/0lSGWAnnDCsCFLPs/kDvq1X8DH/4Fpj0gkualkWsCg42DMN6DXOCMmN2/kNl3FFPj4dpj1NFRtbPrntWgPw06DsZdAx92SrYRzc/fZyzD5Lvj8ZVkrTaVdHxhxlqy7ks7JdnQDT4yM6Y/ClHvFUG7O8dFtBJSdAyPPg0wR4CQzu5Yzgqs3w+S7Ycr9sGJW0z/PcaDPATDqfBhykvy7pO7X3H7asFTOuqkPwoYlTf+8dCEMPBpGXwj9D062AZP73ZbPkrmb8bhEkZtKURsYcjLs+U3oMjTRc+f5AemUy/tzVvHAhIW8Mms5VV4Dzmwj6Nq6iJNHdee8cb3p1qYYPwhJuQk866A2ezHraZh8D8x/q+FAQGPoPEj0xKgLaqsxkhgQDUP5y6+EKfeJnlha3rzP7DVO9Ovw02oqf/TYJnocjTCQCN6bf4TZLzZvEdZH6UjY71oYfEKyDL8gkMzD56/A29fBoo+ilzHgMDjwx9BzrCz6pBh+gSeH05T74P1/wqrPo/38VAaGngoH/Rja9EzOmgNxxAIfPvgXTLhNIlRRUtgaRl8A+39fFEmSjObAkzLGt/8ia696c7Sf37oU9roc9r4McJNl+IUBrJ4remLWUzUljhHSeTDs+10YcUay9ETu3F7wLrz1FzH0oqbP/nDgDyVDlCA9kXMAni5fws1vzuXTZc0I4NVDynU4akhXrjl8N/p1KsFNyLwB4ngSwke3wIc3Ny8gUB+ZFjDyHDjwR1JSmjQ9Ub0Z3rkeJt3VcManqbTsBHteCvtcDW5GuZ5Q62jkFuLrvxOjJWrFsS0Dj4ITb0zGovQ9KUt5/lqJTOUTx4Gx34TDfyfKN+6ZocCXGu4nLs2Pc1aXdBEc8nMYd6UYSnFedzkDYuEH8OTlsHZ+fuW17AjH3lATIAjife8qZ7hOfQhe+jFsXZtfeR0Hwsm3SZYo7tG+XET0nevFQWtOxrEx9D0Axt8CJV3j72z4HvhV8OKPJOuYb8rOgWOug1Rh7J1cPwhZubGK7z1azntzGiifjYhMyuHKgwdw9SG7feXeR2wJA1g2XXTsyk/zK6uoLRz1Ryg7O/4Bgpye+/Q5ePa7sHllfuW16wMn3gR99lUaIFDnaISB1HA/eanUJ6uiuB0c9ScYcWY8F2VuIX7yHDz33fwvxLq06wvjb5b0WxzJZTE+vEmc29zlKRX03EsMv7hmN/ys7NlXfiFZDJWJzyEnw3E3SM1pHI2XXBbjmaskY6sKNwXjrhZHFyeecxcGkm184tLmlw3sDAUlcPhvYOzF8dQTOaNh7hvw9JXRR5O3R+tSOOFGGHBoLLMbuSzGAx99wR9e+JRNVZ4y2UNKW3PDGWUM6BzT7IZfM1dv/gHe+3v+g8d1GXgknPjv+AaSfU8aHT1/LUx/TJ3crwSS00r0hDpH480/SXRK5UKsyx7Hwil3SHQ+Losy8EXxPn2F3GHRgePAuKtECcepht73oGoDPHQWLPxQzxgyxeLkjr5Qj/ymEnhi7D10tlxG00HLTnDqndB733gZfWEgpY1Pfiv/WYyG6LQ7nPUQtOkVP2fjg3/Dq79u+GJ8vul3MJx+DxS0iI+eyDVUeeEHMPEOfeMYfREce73oiJhk1bwgoLI64Ir7J/H256u0jCGTcvjBkXtw6QH9tMhvMr4nDu2DZ8CKT/SMoagtnHQz7H5UbNYcILbdwg/hsQujL0VuLO36wpn3i77I81mXX0cjDIEQnrsWJv03b2IaTffRcN5TUutnugIOfFG2D5yRnxrbnWXYqTD+NqRFneEb2vdg8wq461h9hnJdDvk5HPAD3aNoHIEvTQbuO6V5F0ejIFUAp/4X9jgmHkokDGHawxIY0BVQydGyI5z/jBIlEhmv/hrevUH3KOTuxgXPihETBz0RBvDI+TD7Bd2jkXLlM+7VevG0sXh+wMZKj7Nv/5BPlkZ7F6MpXLJ/P3527CDdw2gcuWDU3cerrbKoDzcFx/9LSqniEAgNA/jsJXj0QmnHrZOiNnDOY2Ib53G/5j+j8dTlUP5AXkXsFF2GwjdeEmfD1IMw8KV05Z4T8n+nYGcYdLxE+0zObPgebFkJtx/WcItfHRz4Qzj4Z7pHsX0CT5yMe06M/tJyU3FTNc7G8WY7uGEA5Q/CM1eqLTPbHkVt4MIXpD2k6QbzSz+REkdTaN8PvvmqzKGpjloQyLp78HSY85ru0dTS72A451Gj37fy/IBNVR6n3Pw+c1cactYBF+zTh/87YYjuYWwf35OW0v89Wn8wKofjwPH/gJHnm2ubgNh2s1+ERy+ouYtmAJliCcD3GJO3sy6/p8CLPzLLyQBYPgPuO7m2Bt1EwgAePNMsJwPgk2el/tfUjRz40ub3rmPNcjJAur+8/y/do2gY35O7U/edYo6TAfKdPn4xzHtDf5agIQJf7lA9e7U5TgZI55J7T4T1C2trqU3kjT+Y5WSAZELvPl7udZm67hzg8W+Y5WSA7NVHLwQM2gt1CIKQKi/gnNs/MsrJALj7/QX89WWFd1h3Ft+DDYslCGqKkwFy7j77XWmpa+p+DXx5d+qxi8xxMkDOuPtPgxWf5k1P5MfRCDyJ7n10S14+vtksmiAXcEwsxwhDeOWXclibSPkD0rrOxM3spiRSsHqu7pHUzyu/gAXvmWf0hSEE1eLcRt1WLwr8rHyvm1eYt+5yL3o/eal5YwPYvAoeOB0wMKjie1JC8NafdY+kfpbPlLs2Jma+wwDe/qsYViby6XPyvRoYzHNdh+89OpWZFRt0D6Vebnx9Dq/MWobnmzd3EEo5t+5yqfoIAzmH1y4wy5AH0Q1bVkmJo677Z9ujaoPctfGr8hIsi97SDnzYskZaOppM+f1yadMko8/35MVbUx20HK/9Ri6BmbSZAx8+vsOM+ywNEYZSShhkzYp8Ow689FMxmE2laiM8dYV5Rp/jinJT2dFsZ1n1Obz2W7PWXBhAdgs8823dI9k+nz4P0x4166zLla68/RfdI9k+71wvl4QN0rGeH/DctApemrFM91C2y0+fmMGWap8gMGzPvvH7/LevbQ5eFTxxiXlBZDcl+svEQF6O9YvFbs9DxUr034abkvIak9JqDfHM1fKcuylRl9CXCJop42mI7BZpP2nKZg582LRMMkGms+4LePnn5pSf+R7Me8uMZg07Yu7r8piRKZmDwJcuSYsm6B7JjvngRqiYbI7R57jwwvdg03LdI9kxL/5ADART1p3jyPmb7/dFmkvg1egJ3QMRgiBkY6XHL56aoXsoO2Tlpip+/tQMXFNeD/c9WDpVHrs1nSWT4L1/yh0mEwg8edNmzqu6R7JjJt8jejZiPRGtpRh4Ulrz+cuRfmze2LhUlIgpBvOrvzKjS1JjWPiBZF5MUL5uCp68TB40jAMT75BXe3UbfWEoqdKnr9Q7jp3h5Z+bUUKVK5l6/Xd6x9FYwkCCGCaUUPmeXIjU1bJ7Z9m6Fp6+yoxsWhjAO38Voy8OLJ9hTAmV6zr8+InprN1iuINWwzNTK3h5piklVKGcH7rP3cby5h9g7Tz9mcjAl/LVl36qdxw7w9NXRV5CFa2F7XvwvxhNKIhjtGSS3gUZBHKv4KNb9Y2hKbz2Wylp0YmfhRlPwvy39Y5jZwhDeO4a/Z2AwlDqvNcv0juOnaFqoxzauo0+Ny1BCq9S7zh2hlWfS0MC3crXceTNhzgx+wWJSOoMDoSh1Ma/fZ2+MTSFd/4GG5drLd3z/ID35qzifzPNLpnall8+PVP3EOS8+PBmtQ8tNxevCp7/vv6OcW5KgmNVZt4HqpcNS+TduwibOUTnaPgeTL5L3yNVzeHdG/QuSMeRMRgQ9dkpsltgwq16oxypDLz/D33ym8qqz+QirE7Dxa/U+8BXU/nkaVi/RN9+CXxRup+/okd+c/joVr13NfwszHg8Xs5tjnf/rjk4EIqjaHrJ1LYEXk3Jjb51l0653PTmHG3ym8qyDZU8Vb5Ef1bDtK5wjWHeG7B8lj77JAxhw1KY+aQe+c1h0l2QjS6IFp2jkUrHLyKf49PnawwXTQdh5XqYHpMygm2Z8B+0KZDAl/r4iil65DeXD/6tz3DxszD5XrMvpzVE4MOH/9Yn33HlzkMc2bQcpj+mz1hNZeJptIC0plzxiT4H16uGSXfrkd1cptyr7XGyIAiZu2IT781ZrUV+c7nz3QWkU5rKu3MVAxuX6pHfXD74l77sdxjKWac7g9wUqjZI4iCiQGg0qzfw4Yv343O/YFvCAKbcI5exVeNnYeoD+l+IbCqbV8JnL+uJzDuueN5xZcE7+hzcVAam3KdeblRMfUijwVcFM57QIzsKptwr379qgkA61sQ1MAD6zhs/C7OeilcJRl2qNkomS5OD+8AEgzvq7YBZSzfw6dINBNr0xD3q5UbFzKek8kILIUx7SJPsCJhyX2SB0GgcDceNt9EC8u6HjvKpVEaMpjhTfr+eyLxfbW4f+cYQhmL0qXZww1AMvmXT1MqNki2r9Ti4flbWXFwaD9THwvfFwdXB5Hv1yI2K6Y/oCwyY9vjtzlL+gB4HF3i6XNN6j4iHJ2oqNdy4TAJicSW7RTIyqh3cwJOHNDetUCs3SpbPlL8iOO8icjQc6aITZ9Z9oSc9WLUpPh1EGuKL99TLDEOJjMbZ4APZN6od3CAL895UKzMfLHhbfZtgNy1y40wYwvw31Stf142/ntiyRu5XqSbwYNGH6uVGyeIJWjIa81dtZtUmAx9J2wk+nLcaV/VZ53uiJ0x6f6cpLHhHg4PrxF9PgHz/QfP3bDSORuUGMdTjzuKJai8OhSEsLVcnL19sXas+QhpkpVtY3NHhZKYKoKJcvdyoqShXX3/rOMmZO0fx3PlZWDFLrcx8sHiieoN51efxLa/N4WeVP/bm+QFTFsWwQc02fL58E9We4lJRx0mGfaLjd3BTydATS8vFXmgmzXc0cpHlJFBRjtKLzYGXnLlb/LFaJy1VkIxDsGqDnhe5kzB3y6apv6fhV5v9Mm5jWVouGQaVrJwt8xd3lpardXD9rDg3SWDJJKVOmus4TF8S03stdfCCkNnLFbeST4qxvOozPU563CtVILLvv/maJvDkxdkksHSK2jKWVCY5jsbScpR3n0rCIQg1TprCuwZelZ7yj6ip3gxrF6iVuXxWPLuIbMuy6WpfzvWzsCQhxnLFFLWPvLqpZAQGQHkW0nUdpi+OYWe9eihfuI6syja3YRDve3w5Ah+WKX4Nft3C+DZuqMvqOZDd2uyPaf5pmcqI0koCSzVsqiTNnUonzauSTZAEVK+7FZ/E54XXHaHysU0/m5zAQHYrrJ2vTp6bSs5Zt3ym2kya4ybD4ANYNlWtkwZ8sjQBBh8ws2I9aVfhPY11iySYkwQqJqvLpgY+LElI8D0M5LxrJtHs+Dg+0lcflevUy0zM3Cn+Pao3xe+Bw4aoXKfWSduySp2sfLN1ncJ1ECZnv4La38VxkzN3XqW8aaGSpMzd1nVKxVV7AVuzyQiqrN+axVF5IXzrGnWy8o1K2y4MkrNfIRJ7IRrrJoLUihH4WSknUFm77EX3+qJWInxF0kh5+UTl/gmD5OxXkP2jsmLPS9Dcqe4vn6R151dBpkidvKScd4r1XbWiUqNTRnVnYJdWeZXRq32LvH7+10jKmoOa30WRkxaGCdMTlc22iyMKo8a8/VldVF+QjHvrOG0kaN4cR9aBqmhVktac0paPTrLmTpXi/RI7d00nIXOnOAsdKtqvw3u0Zb/dOuZVRssCxV3idDxgnC8cB2V7yCFZesJxae7cReNopAoj+Rjt6HiqPl0ACWjEEkULNKPl5RM3o85gdlxIJ2S/gpScKbP5wmTNnfI9m6C5U/32TVLOu7TCLBCQSakJHP7qmebXse+II4d04dbzxuRdzpco/q7yiur9miQ94aaafa8qml1Y0DKSj9FORnFqEqCgRL3MfFCgeO50fFf5oqCF2khfYX5T/ErJtERpdDkpZx2o/12SMneOIwEilSRFTyg+twvSLirvT+eT4oxiYzlJekLp2eMm56wDWQfNDIRG09620x7N/hgj0PF7dByoXmY+6DRIsbFcAq26qpOXTzoNUtsFqvMgdbLyTdeh6l59ddPQebAaWfnGcaHDAHXy/Gxy9ESHAeojpJ12VysvXyheA67j0L9TMpy0gV1K1La3bddHT5VHPug8WN2eTaWhy1A1slTQeUizPyKCjIYDpWXN/xgTKC1T3FveS9jcKa7p7FamVl6+6DFGnbEMUNwOWndXJy9fuCm1hr/jQukodfLySceBai8zu2nonpC5U33u+NXJ0hOKX1Uf1qONUnn5YnjPtqRUpmfShckJDnQfpbatcqc91Or0fNGqK7Ts0OyPaf7MuynoMbbZH2ME3crUXoByHOg2Up28fNJdsbGcFCctldFzmCdh7jrtob4Wtqg1tO2lVmY+UP39O05yAgOlZWpfOHcz0H20Onn5pHSU0mxQ1g8Y1j0hjkaPNrgqm1+EYTL2bOvuElxTSSqTjOx3RN9/NC5e61L1X2Q+6DFWrbHspiSaHXfShdBJcQmY40BpApy0zoPVRz78bDIUSLcyPd09kjJ3Ko1lkHLH9v3UyswH3UeL8a8Kx4GuIxR3WMsTpWVKf4+06zCiZ1tl8vJFj3bFtC5SrCeChATzdPwOSXHSSsskqNtMosslxd1gLmyltmY5R6uu8b9r0HW4+pplNwU99oy/8u0+Rr2x7KSg515qZeaDHmPUvQqew88mI4Pbcy+1xnKOuOuJVEaP0V/QIv5lLB0GKL9g7DgOg7u1pkBR96l8UabDWUploOfe6uVGTfcxysv1CLxk6ImIbKxodp+fhaGnRvJR2hh8otoavhxBEP+5G3aq+o0M0KI99DlAvdwoGX6G+hfOXRf67AclndXKjZJUBoacrD4blMrA8NP1nBVR0a5vTc2yYmM58GDoaWplRs1uR6rvsAc1c3eKerlRMuw09YEBoCiT4tBBMT7rgJPKuuOpvAieo9tw6NBfvdyocBzRscrbUWfEpoxzm9uWHaHfgZE0BIhGW6YyMGR8vNuhjbpAvcEH0plz9AXq5UZFqgBGnKXn4pOfhZHnqpcbFe37Qa+99HX2GH6GHrlRMPBoKG6rR3arrtD3QD2yo6DsLC0GH24adjsMSrqolx0Vo86LpJRgp3HTMPK8+Dq4jgOjzldv8AFeEHDG2J7K5UZFh5YFHLxHZ9I6sjKBB2XnqJcbFb33gzbd9VQ+FLWG3Y9RLzcqhkUXUItu5aYy8V2QXYdBzz31GHyOKx1g+uynXnYUDD0FijRdtss5uHGNzI/9ph6DD+Tg3euy+Boue1+ub+78LOz1LT2ym0uqQNadBoMPkDLBMd/QI7u5tOsDux0h7St10KprfA2XgUfJXU4NpF2XAwZ2oneHeL69dPZeGptPuGnZr3F9vG+vb+mptgDRT3tdrkd2c3FcsQ8iquqO0MpwYN/vxLPv8rir9S1GkAjZPt/RJ7857HeN+ra2dXEc2PNSffKbSlFbOcB1GXyOC216wKDj9chvDqUjofc++uYulYHdj9Zzp6u5DD8ditvrk++mRIFlivWNoansfbmerHeOwJPzNo7s+119gQEgCEIu3q+vNvlNpTDtcvF+fdW2td2WorYw4kx98ptK+36wx7H62sy6aalYiGPHuN2PgXa9pcw6AqJzNBxHIhZxi1Z1HS53DHT2PE6lYeAR0HtffWNoCsNPl4ekdDqXbhr2viJ+70Ic8H2JLusk8ODQX8avjvSw/9NTvlIX35NxxImClnDQT/R06qpLUWvY52q9Y9hZ2veD0Rfpc25BZPcYI05unNjtCOi1t9a5S6dcztqzF/06xuvF5ov360ubYt3vMYRw0I/jVxp/6K/0BgZA9MThv4lX05pUgcxdhIGBaOsmwhAO/51cNowDqQycfJt+xQvypY6/FTIxSe+WdIFjrlf7wGFDpArhxBt1j6Lx9BgL467Un/1z02JAHfQTvePYGUZfKBfUdJWv5EilYdBx8bqge/hvoFW3yKJUTcZx4YAfQZfmvzirBMeVs9kxIFsf+HDCjfFpJ1/UFk78t96sdw0O8LczRqAzObAzDOxSwrWHD8TRbaQ6LrTsDIf/Vu84dobBJ8GQk/QGBkD0RJ/9YHSMAvAH/gg6Doh07qLVOI4jgxt/czw8uAN+IBF53UYLyLy1LoXDfqV7JI3jhH+KU6TbaAH5/vofIpclTSddBCf/R3+kJYfjSsljHNK7bXrCUX80IzAA4mQf+zdo2Un3SHZM3wNq7mYYYCznOPk2/YZAY9jrMrnDZ4SeSEkThKP/onskjeOoP0p3QAPWXTrlMqJHWy7ez/y3XNKuww1nlOkeRi1uCsZcBP0O1j2SHdOyIxz/d3N0bBjCkb+Htr11j2THlI6U8syI725GbyWm0tBrHIy9JPKPjpRuI2D/75t1GTZXv2x6CdXwM+RynwmKN0cYwtF/Nr+E6pCfy8vSJhlYYSDOj+klVCf+W95+MCWI4bpQUALH/V33SLZPQQmMv8WIqPKXpNLyWOX+1+oeyfbp0B8O+7XuUXwVNy1lq6ZfDN/tCCg726izznEcfnjk7vTvZHYJ1bcO7M+gbq31dJpqiMCXILLpJVTH/k3GaIpt5ziit066yRzdVR+pAhh/G5HdAK9Dfr6JMJQ0m6kvNxe3g1P/a05ktC6BD6f8x9wWkJ12h2OvNydakMNxpITq1DvN7ZCx2+FmlExti5uWckeTo6T7XVNTMqW7XnkbciVUY7+peyT14zhw/D+gpKt5685xJU1vase9ghI49S4zSqa2JfDF8W7XR/dI6qdtLzjpZrOc2xocB248exQlheY4QHUZ26cd1xy2G65pRqmbkhKq4/9ljhG/LaMvkvcrDHJugdoSqv2+p3skDXPUHyMvmcqRn9XiODKx5z0lhqlJFJTAeU/KjXqTIvI53BS07AIXPGdeHW7b3jKuTLGZB00qLfcfTr/bvIOm9z5wxv35CBZEg+vK/YdDf6l7JF9n1AXmRZW35ZjrzLyvcfRfZFymORlf4sA5j0LpKN0D+SrpQjjrIblHYqqeKGwNFz4vbW9NoqSLjKuojZHrLp1yGdilFXdcOIbCtFl6bEhpa+66aE/99zIawk3BkBPhmL/qHsnXGTIejvubmQHkHIf+wsyGSQf/TIJlebLr8rfL3DQUlsCFL0DnQXkTs1MUtobznpB3M0wzROuSSkOHfmLUt+yoezRCu75w0QvSGtPkuXNTkrI/7S79XZ1y9NkPzn1c5s2EOy3bY//vyaFjCqPOr6m3NVh5AODInQNTnA3HkQjVnpeana53U7JPz3/anHtCmWI488GaFsrmGcpfkkpLpuqiF80pGW3VTfREq1Lzso91SLkOY3q3584Lx1KcMeM7Hta9DQ9csjeFGVdvO9sd4bgw9mIJrphytgwZD6fcLn9vypga4tjrzXI2DvoxHPjDvIpwwjDPGtz3ILsF7hsPiyfmVdR2adlJMiyd9zDbUK6L78H6hXD38bB+sb5xdBkC5z8jHURMjO7VR+DDgnfgoXOgepO+cex+NJx+j5RfmGy0bMtHt8FLP9JbIrfP1XDE78TJMF15QO1cPf89mHinvnG4aSmXKjsnHvMG0nXPz8KDZ8K8N/WNo6gtnPMIdB8Tn/3qe7B5heiJ1XP0jaN9P7jgWXF+YqIn/CBk2uJ1XPjfj1m/Vd9bWuP6deCOC8dQkHZJmx6MyhGGMO1heOYqve+QjbpAglFgZqXFtuT02au/hndv0DcOx5VL6ntfkX9ReXc0oLYf71t/gXf/pn5RDj5JFmJBq9gcgF/ie+BVwos/gPIH1Mp2XBh3laT7cOM3d4EPG5fBk98Sp0MlBS3lnYU9LxEDNA4HYF3CECqmwJOXwqrP1cpu1VVaeO52uFq5UTLraXj+Wti8Sq3cLkMls9J5UPzWXOCLAn7/Rnjj93LuqWS3w+XeQ4sO8QlG5fA9CLLw8s9h4h1qM4COI+07j/y9XHqNmZ7w/IB1W7P84NFpvDF7hVLZhWmXaw4byKUH9CMEszMZ9REGsHI2PHEpLJumVnaLDpIdGDI+PsGobZnzKjxzNWyoUCu3wwBp2d19tJJ5U+No5AgDWPEpPHEJLJ+Rf3ktOkgHgiEnxdPYy5Eb++evyKLcuDT/MjvuJguxdFQ8N3COwJfI5IT/wKu/gurN+ZfZZz+Zu1bd4hMVrQ/fAwJ47bfwwY1qshsjzpKUfLo4dgbLV/A9qN4Iz34XZj2Vf3luWsrecinwuBnKdQl8WPeFGC+LP86/vKI2cOQfYeQ5tedFHMkZWwveg6evgLUL8i8zd+m7z37xNfaQzEbKdXhi8mJ+/exMNmzN/4OgI3q04YYzyujToSVu3ByMuviePFLy9l/hnb+qCSQPOgGO/ycUtYr3WfdlIPmHUH5//uU5rmQwDv2lVFko0rFqHQ2ozW688zcxXirXRy/DTctr30f9SdqcxXkh1iW3KF/5BUy5D/zq6GUUlEhd98E/ARyj62x3isAXB+2F78NnL+Un4lfSRd5m2fOSeBss2xKGUDEZXvgBLJmUHxkdB0qZ1MAj4x0UqEvu95j1tKTJ18zLj5xe4ySyF8csRkMEnvwuH/xbygu2rI5ehuOKwXLMX+KZxWiIXHbjtd/ApP9Cdmv0MjLFtU0aYpjFaAgvCFi3Ocuvn53JC9OXEuRBTbRrkeFbB/bn0v37EYShWS1sm0Muu/HC92HBu/mR0a6vGMlDT5Z3jOJSZrY9cnpizqvwv5/KHOaD0lFy1nUfozwgoN7RyBEE4FfCpLuknjmK8owWHSQius9VEk1OisFSl9zvtHkVfHgzTLkXNi1v/ue26yNdh8ZeAgUtkjdvIMaLm4bVc+H9f8KMx6FqY/M/t1uZOBfDz5B5S4qDURc/K07nwg/F+PvsxeZHrhwX+h0kb8cMPFIMpIQYLF8hZzR/+jx8dAt88V7zHd10IexxnNxjKR2Z4LnzZf6m3CclQctnNv8zi9rC8NNgn29LRD5JQYEcufVVuR4m3AqT74nmnl/r7tKgYa/LJBMEsc1iNEQuu7Fk7Vb+8848npyyJJL7G7t3acW5e/fm9LE9SLuGX/huKrlzaOlU0bGfPBdNCWTvfWTNDTpebKCkBAXqkpu7z18R227eG82vIkhlYLcjRU/02rtWjytGn6ORI2f8LZkEM5+UuvCl06Bqw45/NlUgF5W7lUl97cAjawxkJ3GH39cIwxqnw4E5r0mUvmKKKOLGbOyCltB1OJSWyR2WXnvXfhdJJwwARzJCs56WDV1RDqtmN67ve4v20G0kdB8Jw06XFs6aNrBycmtk6zqY/gh88QEsLW98tL51d1lzPfaEEWfKfYykGsnbkvs91y+GqQ9JWdDScrlHtCMcB9r3l7nrvS8MO02ytbvKns3N3bLpMOMJybAtnQpb1+74Z920ZHu6lUH/Q+TdEzctraaTEBHdEbm7L/PfgU+fkzW3bHrjMh2ZYunS2K0M9jhWXpgPw+Q5ZvUQ1JhGfhDy8qzlvP3ZSmYsWc9nyzeS9XdsNrUuTjO0tA0jerTl+BHdGFzaBs8PkpPB2B45571qE8x4TO5IVpTDmrmNC7K06iqPKvcYC8PPhLY9dx0dmzvrNi2HqQ/Doo9kzzY2UNCur+iJXuMk+FncVrue0O9o5MgZeLkDbN1CUcRr5kG2EvwqSdFmiqSrRY8x0HF3+ULCQDIku4KxUh++JwrTcWUeV30OSz6WC0ZelRjUqUKJgrbrLZu3XZ/aPw+7hOKoFz8rG9BxZK6WzxQjZvMqcdgCT+Yt00LKe3qMkWwZ1JQBuruGsVIfdQ/+qk1yGC6dKlFUr0r2ZaZImjB0GQLdR9W+DbOrKI36CENZO7nff/NqWXPLZ0qHNK9K9ma6UJRE6UjoOkIyjbCLz13w1Yjm+iWiJ1bPAW+rzJ2bkbkr6SyXHTsPkqDUtvO+q1H3vAoCMfqWTIR1i+Ss86tlntJF0KaH6In2/Wv/PAmNJDeCrB+Qdh0cxyHrB3y2fCPlC9exalMVVV5A1g8pzLgUpVP07diCkb3aUdq2GBBHxYF438NoDnXPq+otcmm8YgpUrhPbLgxkvxaUQOfBoidybf19T2yTpAeOG6Lu3G1dB0smy/3m6o1y1oHs16LWoiNKy2pfbjdIT5jjaNRH4NVGnx2nxhMOSdTdgXzhZ/lyrr4ydzHsHqWaL+++bDN3cWtRq5owlNpwHPnLoXbu3HQyy/GiIggg9KhdcwA1aXM3s+sq2sYQ+BD6WD3RBHINH+zc7TRZX/anU/M/OUvKdZxklkVFxZd6Ar6uY2Pw1pROwqAmYFBXT+R0rLl6wmxHw2KxWCwWi8ViscQS6zpaLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokc62hYLBaLxWKxWCyWyLGOhsVisVgsFovFYokcMx2NwJO/tkcYgJ9VM5444WchCLb/ZwIf/B3M766IXw1huP0/E3gyf5ZawlDmbkf4Wdm3llqCRp5jjVmbuxpWTzQdvxHnmNUT9ZL1A4Id7EUvCPADu1+/ws7oiR3ZMLsajT3H/KyReiKtewCAHGiOC44D6xbC4o9h6VSoKIe18yG7RSbQTUG6GFp1gW4jobQMuo+BTgPBTcuhmDLjV1JG7ncOfFg9p2buymXuNlSAVylzly6QuWvbS+atWxn0GAvtesvcB77M766En4VUBrwqWDELFk+UuVs6DbasguxW2eCpAihoCR0G1MzdSOgxBlp1rTWcHTN99ryRW3fVm2WtVUyGiimwfAZUrpc5DUNIF0JhCXQeLGuudBR0HwXFbcVIdHex/RqGsmbcFGxZDUsmy9wtnQorPoHqTeBVy1mYLoTidtB1mMxd99HQbThkWtSu3V2JwJd5cVzYsET2a0W57NnVc2v0RHWNniiClp2h24haPdF5kMzZLjl3NXstCGDtPNETFeWy7tYvkrMuNy+ZYmjTo2a/ltXoiX7gurvknvX8gHTKJesHfL58E+WL1jJ9yXpmLNnAyo1VVHk+fhBSkHYpyqTo06Elw7q3YViPNpT1bEtp22LCMCQIIeU6un8dteTWVHYLLJ0OS2p07LIZsHWt2CdhKPZJQQl02kPWXE5PtOjwVftwVyK31yrX1+qJinJYMROqNomOBcgUQWHrOnpilPx/YYkxNrEThhrdn9wkLPwIptwDnzwHlet2/nPShdD3QCg7B/Y4VhZl0o3mXCTv85dhyn0w93VRFjtLYSvY/WgYeT702VcUkQELM2+EIRDKJp3xOJQ/AIs+2nFktD5KOsOQ8TDqAugyJPkGTM4Z3bIGyu+HqQ/JodeUI6R9Pxh+uqy7Nt2Tb8Dkzrq1X8Dke2D6o7Dui53/HMcRJTL8DCg7WxQMYbId3dy+qiiXuZv1lDhpO0sqA733hRFnw5CTZL0l3YDxPXEQ5r4heuLz/0lwYGfJtIDdjoCR58KAQ2uc5QTvV8APQvwg5MUZS3ls0mI+nLearL/zZ127FhmOHtqNM8b2ZETPtl86LoklDAAHqjaIjpj6kDgXTclmt+0FQ0+FUedD+77GGM55I6cHN1TU6IlHJIiysziOBPeGnwkjz6l12DTZxXocjVw6dtpD8MGNEsmLipYdYewlsNdlYkQnzeEIPDGSJ/wHJtwqCzIqOvSHva8UZeKkkrehwwA2rYD3/yWbuGpDdJ/dc0/Y59vi6CZNCYehHFwrPoF3b4CZT0RXjuI4MPAo2Pca6LWXOLpugpRw7nCf/w68dwPMeS26z04XisOx73dl7yYN35P1MfNJ2bNLy6P77OJ2MPoiGHel/H0S9YSfhUn/hQ9vaZpT2xBte4l+HfMNcd6SdNYhDsb6rVnueHc+93/0Beu2RFd6N6S0NZce0I/jhpcShmHyHI4wlCqUd/8O0x6WjEVU9D9Ezrp+ByavAiOn9xZNgPf+DrNfjK7MOJWRgOi+10CXwbX6XCF6HI1FE+DJb8GaefmTUdgKjvgdjL4wGYsytxCnPwYvfF/SjvmiTQ848d/Q7yAtizJyAk8il+/eAG/+qXF1ok2ldCSMvw06DkhGlNn3IKiGl34Ck+7Kr6zdj4ETb4SiNskwXgIPNq+Cp66AuRE6GNviOLDnt+Dw3yQnQBAGsHwmPHGplDXmi0wxHPIL2PvyZAQIwkDOndkvwrPfgU3L8yerpDMc93cJriQgQOAHISnX4b/vzecvL81mazZ/d/F261zCDWeUMaS0NU7c9SuIngh9eO3/4MOb83sXr99BcNLNUNIl/nYdiJ6o3ADPfhs+eTa/skaeB0f/GVKFSvWEOkcjtxBf/RV8dKu6S6H9D4WTbpJMR1yVSG4hPnM1fPqcOrmjL4Sj/ghuQXyNlzCQ1OMTl0qNowpSBXDQj2G/a+JrvOQczHlvwdNXSh23CorbwTHXwbDT4mu85AIbk++B//0s2szZ9mjfD8bfAj33UiMvH+Sy3W/9UaKiTSlpbAq9xsH4WyXIElfjxfekFv6F78G0R9TJHXYqHHuDlFfFVE/4Qciy9ZVc80g5E+avUSIz5Tp864B+XHv4QID4ZjfCEJZMkuDx6jlqZCYhkJzTbzOekD27Rc260xFIVuNoBL6kvZ+4pGn1Zs2lsLUYzCPPVS87CmY+Bc9fo24h1qVNTzFeeu8Trwh9bgO9ewO8+cfai1MqKR0Fp9wuF+7j5GwEvhh4L/5ISi90sMexchgWtIqX8eJ7ULlWlG6UZVKNxXFhr2/B4b+N3121wINVn8PjF0s2QzWZYjj0V5LdiBthIOvtmatg4zL18ks6wwk3yj2OGEbo735/AX968dO8ZjEaYmCXEv555kh269IqXpfFA1/07Ku/gg9v0tNRsP8hEiAobh8/PZHdDE9fBZ88o2cMoy6Ao/8i85Zn+yT/jkYYwGcvwaMX6jH26rLfNXDYr/WOYWd59wZ49dd6x+CmJVU57LR4KJEgAEJ4+gq5iKaT4nZw7uNygTcORp/vSV3tfeOlxFEnHfrDhc9Di07xUCK+J92Q7jpWXQaoIfodBGc9FJ8a+sCHhR/AA2dI5y2djLkYjv0r4MTjvAOYeCc8/z297aMdB46+Dva8RN8YdoIwDHEch18+PYN7PojwDksTaFGQ4vbzx7B3vw64cXA2Al/uAD10dn7LQhtDmx5wwXMSFI2LntiyCu4+TgIrOukxBs57SoIsedQT+XU0wgBmPAlPXqouBb4jxn4Tjr1e9ygax+u/hbf/qnsUguNIenz0BWZnNnLtQx85X22Z2fYoaAnnPCYXxk02+gJPOtLcdSwsm657NEKbHvCNl6Ckq9kdvXxPLkH+92jYvFL3aIQeY+D8Z6TVq8lObuBLV6SHz9YfjMox/HSJlMbB2Xj/X/Dyz3WPopbDfwP7fkf3KHZIEIb84NGpPD55ie6hAFCQcrn53FEctHtnszMbgS/3HO85UTo2mkDLjnDhC9C+v9nOhu/BpmVw51H6g1E5ugyRuSssyZt9kj+LMfDh81fMcjIAPr4dXvmV7lHsmHdvMMfJADHgn78Gpj9u+KNroaw5U5wMEOP9/tOkd7hJe6EuOeVx70nmOBkA6xfDXcfB1nXmPh7mZ0V53H28OU4GyDsT959W87CdoXs28GDxBHj4HHOcDJA7Ds9da7aTEQYw8b9mORkAr/xS9KyBD4fV5ZdPzzTGyQCo9gOuuH8yHy9YY+5jf0Egd20fON0cJwOk6cbdx8PGpebqicCT5xvuPt4cJwOkTPXek+T8zdNjxPlxNHwPVs6WqLKJhtV7f5dUs4kvPAeeXA7SXS5VH2EIT10mBoyJr+2GIbz+O+nMZRrVm2Qzb1lt5rpzU7Jflyi6ML8zrF0gc4eByjcMZc/mlJxpfPEePHmZmZfqA0/ac99/WrRtMKNi0n/hnb+ZGVjxPZj3Jjx/re6R1M8L35dAo4FGXxCE3PrWXO77UG+5VH1UeQEX3/UxS9ZuxTMxOOC60kVv/tu6R/J1Ni2XczioNtPJDYF7x+e322pTqZgCD5+Xt8x3frSPg0SVTVQeOV7+uRgGJhl9gS+vQJqqPECMgye/ZZ7y9T3ZLO/eoHskDbN1rRzSppWxBD5MulsMA1NZPgPe+IN5685x5CwxUXnkmPmEvEVhWtDHcaUbXNVG3SNpmDf/KHXUJhnMYSC69ekrzdsPOcIQnr0avK1GjdELAhas3sz1L3+meygNsrna55pHynFNy6b5Hsx6Rh4bNZW186XTn2lzFwbSSW/ZNN0jaZi5r+UtAB+9oxEG8NZfzCq/qI/qzWIwm2T0uSnpQpDPNzKiYM08SY8bRSDOrUFKrV7mvCov9Jpi9AU+bF4BL/9M90h2zPv/kHPFFKPPz8KC92DiHbpHsmOe/x5UbjSnhCrw4aNb5AK4yfjV4gyZZLg4Lrz4w2gfa80HG5fJujPoTp+Dw3cfLqfaN2QfNMCkL9Zyx7vzzSmhCgPJyj9/je6R7JiJd0rGxZSqC9+T8qR3/657JDvm5V9IGXDEzka0J4DvyevB78TksvWCd2HCbWZkNQIPpj0Ks1/QPZLGMeFWWPihGUZfGMJrv9XfwaGx/O+n5pRQuSnJspgcVc4R+GL0mVBClSuZevoKM9P027JltTwIZUIJVVDTneu13+oeSeNYWi46zYQghu9J5rH8ft0jaRzTHobP/meEngiCkFvemsu0xet1D6VR/PV/s1m8dosZJVSOC89dI3ch4sBTV0CQNedsfsKwu8oNUb0Jnrw88gB8tFonlYanLo/HhOZ49deihHUvyOxWePEHesewM4ShfNe6I32BLy8Hf3Cj3nHsDJXr5dDWnU3zPWn/O/d1vePYGVZ+Cu/81QAnreY+0NoFmsexE3zyDHz6vP5In5uWzG12i95x7Axv/wXWfqHf2Qh9cRjjxLPflnFrJAhCKtZv5R+vxiQYhdzX+MFj00jrDg74WfjsZSnBjAvrF9Xcc9Vs1wU+vHeD2ChxYf5bUnURYXAguhXse1IWsnRqZB+phOrNYqTqdDQCDyb8x/ySqW1ZM09qv3UaLm5KunPpNgB2ltkvSAZGZ7QqlY5P9rEuH92q31iu2iQp+rjx9l/1tgkOPGk4MP8tfWNoCn625v6XxsCK70kmw/SSqW3ZuAwm36M1q+E4cPObc40vmdqWCfPXMPmLtXqzGqmMONpxY/I9ULlB7xgCTx4zjBvv3hBp9ju6T0ql4b1/RvZxSpl0F/iaL65PuFWv/Kby/r/0GS5hCBuWwidP65HfHMIQ3vuHvlIW35Mo1SpzL0U2yNa1MPlufYZL4EnJZZwi8jkqJkvJo66ss5uWdR9Hpj0k7Sl1kUrDB//WJ785fHiT1rK9jZUej09erE1+c7j17bn6shqBL10mF3+sR35zyG6Ve2C6st++B1PuhS1r9MhvDqvn1JQ8RhPQi2b1hiGsWxS/KFWOyvXyPoSOKKmfhdkvSdQnjiwtlwu6OjIKYQCTDG1T3BhmPK7PWE2lZe7iyqS79D3M5KYlWhZXJt6h7+HIrWvNeuNmZ/CqIi8paDSBD1+8LwZAHFkzT+5EanBwPT/g0UmLqMzGK5uR49VPVrBmc7Ue4Y4bj2YXDTHlXn3l3am06Km4MvHOyILIETkagUQY40z5/Xoi86mMbIY4o8voclNyxyCuZLfoc3C3rjW7ne2OWDELlk5T7+AGvhhM68zrwd9oPnkOqjU4uL4H5Q/E6w7ftpQ/oMfBdVPxdm5Bxq/BwU2nXB6bFM9sBoAfhDw6cRGejrIvrxJmxbBiIMf6xTD/HfVnThCIjjK9++r2mPua3F+OgGgcDTclaZY4s/ADqbtWje/F6zJufXz2Pz0tDFfOhnUL1cuNks9eUu/g+p7IjbPBBzD7eT13XOLSGa4hsltg3hvqM4GpdPz1xIpZ+u5IfB7zufv8ZS1iV26s4pOlMeiqtx1e/3QF6ZRiHRsE0ia2erNauVEz+wWU360KfWm8EWcCH2a/GEkgNJqV62dh5SeRfJRWlparvxS+arb0ao8z675Qf+nKz8azbnRblparl+k4UKFBbtRUlKuPLruphMzdFD0NMOLWLKQ+Fk9U76RvqIhnrXddKtdLibVC/CCkfFHMmqzUw8wKDZeaQ1/udMWdpeXqOzymMsnQExHNXTSOxopP9HeBiYIlk9UqED8rSisJVExRW8bipvQY6VGzfjFsXadWZlLmTtfvYPLrro1labl6J239Yr2XqaNC9brLXchNAos/VqpjwzCMzbsZ22NTlcfCNYrLHZNiLOu6Q5oEHVtRHkm1SvM/wc/CkoQcgkvL1ZaxuKlkRPhAIh8qSzEcNxmHIIiDq/IgDIN4147m2LgMNkdTQ9po1i6Ix+OGO0L13gm8ZGQgQeZO6V2DMBlGCyjXd+mUy/Ql8Xc0AKYsXKv+nkYS1l31ZlgzX63MresksBJ3ls+IpDw5AlclnRyDr2KKWnmOC0sVy8wXFeVqnbQgkE2QBFQ7aWvmx7/uNseSSeruafgJMpY3r4RNK9TKTIqeUH1mJ0nHLi1XfiF8RkIcjelL1uOo7KC0eXV8u2Fuy+KP1XWLCwMJHiaB7FZY0/xOd813NBwHNiTAcwPYsES9zPUaZOYD1Wugcr1sgiSwYYnaGtK4X6Cvy/pFECpUIEnZr6A24uamk6MntqyRVrcqScrcKY7yen7Aqk0xvwNZw9J1laRchY6GDnsoX2xYAigKSAW+6KWksLb5HRajCS2oPnTzhY7fw85d0/ATMm8gLQRVde0Kw3g+NNcQXiWoutPs1MhLCqp/l6ScdSANPNKF6uQlZe4UrzlVL4EP6FxCuxb5zej3al+c18//GkkJ5IHidRcmT0+EQbNslGgcjbh3TapL4KuNLidl7lR3YUnKvIGsuTBU87BQGCajcUOOMECpp5GkubN7tumovlyalLlTvH88X83Z8P0jdueooV2VyFJGUtYc1OxXhdmgpM1dGDZr+qJxNFIKIzv5xHHUt0FLFyYjwuwqfgsiKWsOpKxEVe2t40C6QI0sFbgp1CmQMGFzZ/dsk3FUt8tMyNyl1Kc076cAAIpuSURBVO6fjKK3J371zAz+8tKneZWxz4AO/O6kYXmV8RVUZuzyjZNCXUCKhM2d2+yKi2gcjUxRJB+jHR2HeTohc6d6YyVpI6eLmp2abDSOA5mW+ZejinSRUj+DtOLyhXyi+txOip4A9Q5nUuZO8e9RkFbjaCzfUAXkt7xtcGnrvH7+18gk6KxTamc5CdMTxc0OhDZ/F4YBtOnV7I8xgrYafo+2PdXLzAeq566oNRSUqJWZL9r2Utt1ql0fdbLyTds+6rrYuG5y9iuo3bOBlxw9UdJZeWSeNglZd4r1RMp16NI6GUGpHu2K8VV12IPkrDmQdafqHqSbhna91chSQft+zf6I5s984ENpWbM/xghU/x5BAN0Uy8wXpSPV1iU6LnRVmEbOJ91HqW352K43FCqOjuWL7qPUKpAeY9XIyjetukGLDgoFOsnRE6rPbN9L0NyNVH43aFj3Nkrl5Qv5PRTeMyhuC216qJOXT3qMVadjHSc5dl1BSSSByeZr6FQGuo9p9scYQbcytcZymCAnrftotTXfSXJwu5Wpu6PxpczhauXlgzY9RBmqllmkWGY+UL133BT0SIieKC1Tayw7DpSOUicvn5SWodJY9vwgMY7GyF7t1La3hWQYzIWt1WcYilono3Kg6zBDXgYH6DQwGTXz3ceoNZaT4qQ5DnQbodZYDsNkHILt+sihpJLAT8bc6foduo3QIzdKupWp76DVqhu0aK9WZj4oHYXSyLKbkkBOEug+WmnDFddxGN6jrTJ5+aJ1cZrStorr/v1sMoJ5uoJqSdCxpWWRlHVH42i4aegyJJKP0objQrdh6iPLHQbE/9JVu75QoPiCcSoNPfZUKzMf6DiMwlBK3eJOaZl6YznwEjJ3o9R3ToJkzJ1iYxmQeyElndXKjJoW7aF1qVKRrutQ1rOtUpn5YGiphqyMm4LSBDi43crU3oGEGictAWddtzKxF5pJNI5G4MHux0byUdroeyBkWqiX66ZgtyPVy42SPY5Tv5EBOvQTRy3O7HGcemM5lYaBR8Y/Czn4RPUGHw4MOl6xzIgpbA39DpTL7Srxs/HXE6Uj9Rj8YRj/udv92EiMlp2lXcuC2DsbRwzpiqfo8cEvcVzoux8Uxbz0bPCJ6mW6aRh8gnq5UZIqgN2PFnuhmUSX0Rh5nrpLmfmg7Bw9j3EFHow6T73cKBl1vvpMEMjclZ2tXm5UFLaWwyil+D0DgMJWsPsx6uVGReko6DhQ/ZmTu2vQcTe1cqNkyEnquyaBrPPhZ8TbwS07W4+eCIME6IkL1D90iNzTOHV0fC81F6RcTh7VnbSiN0G+gpuGoaeolxsV7ftBzz3VB6QcR2TH+V7awKMiczKjW7mtusBuR0T2cUpp2VG8Xh0Gn5uG/odA25i2Q+u1txhdOpxMNy3KS4fRFAUjztCz5kCctLHf1CM7CsZcpO+V7sCD0RfpkR0FY7+pxeADoLAEhpysR3ZzKSiBEWdp0hM19zS6DFUvOwo6D4KeYzVkICGdcjl5ZHdKChV29ouQo4Z2pXWRJj0RhjD2Ej2yo2D0hcq7nH2Jn4UxF+uRHQVjvxnZ3EVnHQYe7PvdyD5OKWO/qeUA/JIggL0v1ye/Oez7HWm/qIuWHWHYafrkNxXH1btf3DT02S+eF5tLOusz+EDmbsxF8Swp6L2vfOe6zrvAh/2+q0d2cxl5rvq7aHXxs7DP1frkN4dxV2vVE0WZFGeMjee7EJcf1B8/UF9yBsg50WWwlJbHjcJWNbadJgczlYFhp0OrrnrkN4cuQ2vKa6OZu+gcDTcNvcdBr3GRfaQSitrCXpfrdTRSafG847YguwyV9FoENXxNJgjggO/rMzqbyrBTpVWqznJDPwsH/FCf/Kayz7f1l2mmimCvy/SOoSkc+CO9gQE3BZ32iF/ZXqaFBFVUdpvallRGzo0IHtBSSrs+UjKnUU84Dlx2YH9aFGjU803goN07Mahba/Vtbevie3JuxI09L9XfaMeh5tyIGQf8INKKgWi1deDB+Fv0f7k7w9F/lnS+btwMHP9P3aNoPG4axt8qhr7WcbiiyOJkMLfsBEdfp698JUcqA4OO03NZrql0HwXjrtQbGABZdwf8QEpC4kLZORKl0hkYAMlqHP+PeGWEDv0llHTRcxetLiGiY3WPo7E4Dpx0s+5R4DgO7VsW8OOj99A9lEbTqjDNn08Zri+bkSOVhj77SplyXOg4EA76sf6AlJuWgFSc7mrscVzNPb7ogrfRfgtuWp6tP+TnkX5s3tj9aBhxpr7UWl1ynYBGnKl7JI1j/2slparbaAE5TPb/XnzayR13gzi3ug9BEEfx+H8ofiW6iaQL4eT/6Hduv8QRZ1u309MYWneHY67T0vXna7gpaXV65B91j6Rx9N5XSltN+J5Taei5V3yyaWO/Cb33MUJPpFyH88f1YVz/GJx1wM+PG0THkkK92YwcYQhH/TEeL4W7KTj5NqI2b5tMGIjeShfpHsmOadEeTvhX5Do2+m/CTcHeV8glYZMpbgcn3KinLWtDhAEc81fzS6i6DJVUqgmG8peEMP428y+GDz1F2qOa4NyCROYLW8Gxf9M9kh1z0E/kzRYDjBZAxtF1WDzupp14I6QKzYmEu2kYeY75DUQyLWoytxrLzerjsP8zv4SqXV84/HdmOLc1+EHI9aeNML6E6sCBnThjbC8znAyQcyNVCCf+W/dIdsy4q+X9B1P0hJuGtn3g4J/pHsmOOeav8oBwxK3P82MphoEYfYWKXzzeGY79GxS3NSNKlcNxIV0MJ95kmBFfh0yxRAvM0R2Cm4aOA+CQX+geScO0LpV1Z0xEvgY3LalSky/V99pbal1N2q8g+/Sgn5p9qX7MN6SznSmKN0fgi+HSsqPukTTMkb+XfWtKYCCHk5IoqamBlVRGSrzctDnOLZLV6NK6iF8dP1j3UBqkXYsM151qQMnUtqTS0O8g2NPgLlRdhkpFjUFrDhDDfZ+rJDtqKkNOlkBoHs66/FizbhradIdzHzPzvsYRv4OhJ5unPEA2c/+DpZzFNFIZOON+ucxpmtECNZ2cvg17fUv3SL5Oiw5wwXPSIlP1Q2mNIQiklrrfQbpH8nW6DIFzHtN/p2V7nPeUmRHm3Y+RKJVBUeUvcVNQ3B7Of8bM+xr7f1+cNNOcW5Dzt3QknHK7eUEpx5VAY489jdQTKdfhjLG9+Pah5j32WlKY5r5v7kX7lgXmZDPqEoZw9F/MfLS0fT84/2nznIwcYQBnP2Jmi+q+B0gAOU9B0PydUG4auo+BMx8wy9k46Cfmtwh0XHkE76g/6R5JLakMnHIn9DvYTMVbl6P/IvNnCi1qjKm2vY1UvIA4P44LZz0sNdWm0Gl3uOBZOUNMDAyAfKeFreHC56UxgSkMOBROvwdwzFW+qbR8x+c+blYGfO/L4VCDs6Mg5/Cg4yUrZIqz4bjS1GTISWYGVOpw7eG7c8n+5gQHWhakuOuisezetZWex/kag+NINcOpd5lV9ti2l+iJorbm6gk3DZkiGadJTUR67S0OkOPmbc86YZjnUFfgw+KP4f5ToWpjXkXtkCN/D+Ou0juGnWXKvfDMt/VGczPFcMZ90O8Q45UHIFEXx4GXfgIf3qR3LK26ycHSvq+5B2BdAl/q0R8+Bz5/Re9YupVJhKqwJB5z53tQuRbuOg5Wfqp3LINPglPukH1gemAAZM2t+ATuORG2rNY7lgN/GI966hxhAJ88C49/E/xqfeNIZaSca/BJ5jq29fCPVz/jhlc/1zqGti0y3HfxXuzRrRXpOOjYIAACeOJSmPG43rF0HAgXPgfFHcwN5NXF9yC7Ge49CZZM1juWAYfCmQ+Kfs2jnsi/owGiRNYugMcvgQoNE1vSBU74J+x2ZKwOQECUyIL34KnLYf0i9fI7Dxbl0XlQPAyWbZlyL7z0U6jaoF52/0Nh/M3xOQBz5JTIm3+Cd2/QcxF29IXS5SRVEA8nI4fvgbcVnv8eTHtYvfxUgbR13O8a+WdTIt2Nwfdg0zIxXr54T7384nZw7PVSpxw3Ah+WlsvcrZ6jXn77flJ6UTo6HsGobXh+WgU/f2oGa7dE93ZAYxnbpx03nFFG19ZF5mYy6iMX/Hzvn/DmH8CrUj+GYafJvcdMi5jpWE/Ou//9FCbdqb601U3BvtfAwT8B8h+MUuNogEys48K7f4c3/6gu8jL8dDjm+vgtxLr4HgTVYjBP+q8amW5aXvA98Cfyz3Gdu8CDzavgqStg7mtqZBa2luzZqPPFAIijgwaiSJbPFONlxSw1Mtv0kGYI/Q6szUzFjTCQs+6zlyQbuWm5Grmlo8TY69A/Xg5GXXL75aPb4NVfQXaLGrl7HCdtHYtax8uxrYvvQejDa7+RTK6KLLjjSqvdw34tF9Rjqie8IGBTpcePn5jOSzOWKZFZlHH54ZF7cOG+fQhDzLyT0RiCANbOgycuURehL+ks91h3P0bkx9C5/VK/zX8Hnr4C1i1UI7fzILlD1XWYMv2qztHIEQawei48eWl+F2VJl5qFeHSt4o8zuUU57y145qr8LsrOg8Vg6TIk/vMGtcbL5Hvg5Z9D5fr8yep/KJx0kzzKF1cHoy65V6Tf+iO8949IXwv9Co4DI2vuJaUKYmuwfAXfE0P5he/nN7uRLpQHK/e7BgjjayjXJfBhQwU8dRkseDd/clq0h6P+AsNPi6/Bsi1hCEsmwpOX5Te70aG/NJDouVf+ZCgkCEJc1+G5aRX88umZrNmcv2Do2D7t+NvpZZS2LY6vg1GXXCD5vX9IJtyrzJ+sYafWZDFaJkdPBNXwv59JIDlfJrmbls6NB9VkMRTOnXpHA2RiU2mpAX//nzD/7eg+u0N/uYdRdo4YeklQunXJGX7TH4H3/xVtpLnnnrDPd2CPmihBEjZxXQJP0rsf3w4f3SKGTBQ4Lgw8Sgy9nnvGO4vREGEAm1fKmpt0d3SlaOkiGH6GZM/a94tvFqMhckGOlZ9KNnfG49Flc4vbwZiLYdwV8vdJCArUJfDk/J7/Drz3d8lIRqWu2vaSC9+jL5K7BYnTE1k5g2Y+JTq2Ykp0n92tTBqqDBkv30fC9IQXBHh+yIMTFnLne/NZtGZrJJ/rOHDAbp247MB+jOvfET8ISCXBsa1LGMDWdZJRm3gHbFkTzeemMtJ+db9rJCKflKBAjpzeWzNfnLWpD0bnrBW2klfd97laAvAa9KseRyNHzuFYM1+izZ88DWvm7bwyKekMAw6TUpVe42o/N8n4Wdl8SybB5HulTGPj0p3/nLa9YdBxshA77V77uUkmF32Z8xpMuQ/mvwVb1+7cZziutPkdejKMPE8eWcwZRkklDIFQ1sjMp+QwXPTRzpe3pAqgtEwcjOFnSMtfwuQZynXJOZ9b18m8TX8Ulk3b+QxRQQn0HgcjzpZ966SS59RuS25frV8semLWU7Dqs53XEy3ay3siI8+Tdo5hkOz9CrXn+bLpMnezX5B53Fna9JDqgJHnQ7fhu4Se8PwA13X4cO5qHp64iHc+X7XTWQ7HgX4dSzh2eFfOGNOL7u2K8fwgXncxmkLgy/769Hkovx++eB+qN+3cZ6Qy0GWYZBtHnC3vniUxiFeXMAAcqN4sweRpD0uQYGfvv2SKJdM44ixpzpAuQGf3Qb2ORo4wrDn0U1C1CZZOlUvjFVNg7XzIVoJfBW5GJrCkizyQVTpSWuiWdPrqZ+xKBL4YaI4j3VqW1Mzb0nKJ2HuVEkFNFUr0uF1viUiVjpS/ilrX9k5OUoSgMdR1DDYsgcUToaJc1t+W1XKpN/Bk3jLF0GE3MZC7j5Fe2Jmi5DsXDZEzNHL1uYsnyppbNkNK07xK2Y+ZYjGOOw+umbvR0HF3CQTsAsZKveR+bz8rmY7c3K34RBSMV1nzeGeRKNeuw2TP9hgr+9dxd825C0O5g+CmxbldOk0CLUunSomQVyl/uRmZu5JOoie6jYQeo6UDHOyaezZ3X8NxxdmtmFKjY8vF8fC21uiJAnk0tk33Gj0xCrqPknUYBtLadBfTE34QflnetGJDJeWL1jF9yXqmL1nPqk1VVGUDvCCkMO1SlEnRp0NLhvVozYgebRlS2obighR+EOI64CQpW9sYcudUGEi59+KPZc0tmw6V6yC7Vf5buggKWkrwLqdjOw+qOSd3gcBxfXypYz1Y+ZmUQy6dKvcmqzfVZjzSxWLHdRlaO3ft+8s+NURPmOFobEsYyuRub4J8Lz6tG1USBKKMtzt3WeNebDWCwGeHNe5+tRgydu6+ip+t6cPdwH4MA5lfAw4949jRfmzMutwVCUMIstt/HTvwUNFVJXY0Zj/myq+SnGlsAn4QEobhdrMSWT8g7Tq7nmOxI3ZktzXG9ttV2dF+DHxjSxnNdDQsFovFYrFYLBZLrLGhCovFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWMdDYvFYrFYLBaLxRI51tGwWCwWi8VisVgskWOuoxGG4FdD4G/z7wPws/L/lnoJwpCsHxAE4Vf/fSD/PgzDBn7SQuB/fX2FYe2/tzRM4MlfX5s7z87djvCzssbq7s0wAL9mTi0Nsz09EVg90SBhWLvuvvLvczrW6okGCRqwQwJf1qOlYQJPzjWrY3eenD7YVk/k5tRQ0roH8CW+B6ma4WS3wrLpsGQSrJ0v/+xXg5uGdBG06gqlo6D7KChuV/PzWUhl9I1fI1k/IJMSn3FDZZZpi9czbdE6lq6vpMoLyPoBBWmXokyKHu2KGdGjLUO7t6ZFgcy35wekU+b6nHml7rrZtBwWT4Kl5bBltay70IdUIRS0gA67QY8x0HkQpApks4e+rMtdjTCUA85NyT+v/QIWfwzLZ0DlevAq5c+ki6CwROasx1hoPwBct1bJOLvgugt8cBz53QMfVn0GSybCik+hehN4VfLf00VyvnUdJnPXpkfNz3vgpOTP7Gr4nqw5x5F5Wj5T5m713Bo9UVWrJ1p2gtKR0H00tOxY8/O7rp74yu9etQkqpshf6xdCtlJ0bKoAMkXQpqfo2NIyKGz19Z/f1aj7u29eBUsmw9IpsGmFnHWBJ3oiUwzt+0H3MdB1KKQLa36+jn2zqxF4tTpy/WLRE8tmwNa14G2t0ROFUNASOg0SHdtxN/mZMJD/ntMzuxJ1f/cggDVzZe5WzJL961XKn8sUQ2FrWW89xkK7PvLvA190jAF6Qu/KzxkrhDDnZZj1tGzg1XMan7Fo3V0OwwGHw7DTxKgJ/MQvzCAIcRyo8gKemVrB65+uYMaS9Sxeu7VRP+840KdDS4Z1b8NRQ7ty+OAupFwHB3AMWJh5JXfwbVkD5ffDvDdh6VTYvLJxP++modMeYsAMOw367FtjdO8CiiSncNd9AVPug4UfwNJpULWhcT+fKYYuQ6HnXlB2lvz9rqKEc7/nsmkw9SFYPEEMZa+qcT9f1Ba6jZD1VnYutOm+68xdzjmb/ybMfFL0xKrZX4/GN0RJF9ET/Q+B4WdCcduvGkBJJadH/azo19kvinOxdn7jP6NdX5m7gUfDkJNqgyxuwoMEOTuicj1MexjmvCZBqI3LGvfzbgo6DhSHbch4GHDormM0586lDRWiJxa8Kzq2cl3jfj5dCJ2HQM+xsl+7j9p1zrqcjl3xKUx9ABZ9JI5Zdkvjfr6wFXQdDr3HiZ5o31d7kMAJddTRhKEojc0r4YObYMo9EiVoLukiGHwi7Psd6DIkkYrED0JSrsP8VZu45a15PDu1gi3VjVS226FNcYZTR/fgkv370bVNEUEY4ibN4cgdVAs/hPf/CZ/9L5qylDY9YcxFsOelEpVJYpTe98Sw+ORZ+PAmmcMo6DIU9voWjDhLzoSE7VdADJbAgyn3woTbYOXs5n+m40Cf/WHclTDwqOQq4TAUQ++jW2DSXbBxafM/M5WB3Y8VPZFUAyYM5Bxatwje/wdMfbjxwYDtUdgKhp8B+3wb2vWWSGvSHI6c3VBRDu/9Az59LppyqFZdYdQFsPflEjRImn6FWoP2s/+Jnpj/VjQleB0Hwp6XyPy56WQ6a4EHITDtIfjoVglIRUHPvWDvK2DwCdoCouodDd+DIAuv/AIm3Z2/esYBh8Jx/4DWpYlZlH4QsHZzlp89NZ2XZy3PSwlt2nUYP6o7vzpuCEUZNzklVWEAKz6BZ78NiyfmR0ZBSxh3FRz4QzkwkmC85AyWz16GF3+4c5HQnaGkMxz6Sxh5XnIykrnfY8Jt8OYfJYOWDzoOhGOvh74H1AZx4o7vAQG8/nuYcKuUReWD3vvCCf+UqH0S1hyIwVK1EV74Acx4PD/3GR1XovTHXA9FrZITIAgCOeOe/Q4seCc/MtJFYjQf8guZxySUo+XOnQXvwfPXwspP8yOnuB0c9GPY67LkBJJzeqL8AXj111LCnQ/a9YGj/yyBqZxeV4Q6RyO3EOe/A09fAesW5l9mQUs47NcSaY6x8ZLLYjw2cRG/eW4WGyrzf+mnc6tC/nzKcA7eo3O8sxu+Bw7w1l/gnevVXKztMhROvk3uJcQ5u+F7Ugf6wvdh6oNqZA44DE66CVp0iLcSCXyJvj95Wf4Mlm0Z8w048g/gZuLt5IahlPc8eSms+jz/8tKFcNBPJMMR5xLInPEw8ykx9raszr/MFu3F2Rh6cryzG4Enc/f+v+CNP9TWv+eTDgNg/K1SghtX/Qq1F5Rf/hlMvENNE4He+8rcxT2QHHiwZa3YxJ+/rEbm8DMkMJUuVqYn1DgauSzGyz9XtxDr0md/GH+LpC5jpkS8IGDd5iw/eGwab8xeoVz+KaO6838nDqUoHcPsRhhIneOTl0pzAZW4adj/e5LdwInnYfj5K/DM1dGUq+wMRW3EYB55bvwi9Dljb8J/4NVfQfVmtfLb9oITb4K++6uVGwW5jmWv/04MPtWdBbuPhpP/I5G/uO1X34PqjfDcNXKHRTWDT5QKgsJW8XNyA18Cn09cIpdtVeK4UtZy6C9lzcXMPgHgi/fhqcth7QK1cjMtJJC817fi5+Tm9Fr5g/DSjxt/dyUqWnWF4/8JA49UIi7/jobvwdbVcNdx0l1FF4Wt4KyHodfesVEifhDyydINnHfHR6zdoq/tW492xTx06d50bV0UH2cjDCSy9+Slelvm9dobzn1CoqZxUiKv/w7evk7vGEacJdmNkHgokVx72se/IRdvdeE4cNj/SYQ+LuQM5XvHSzZDF+kiOP1u2O2I+GQjAw/WzIe7j1cfFKhLSRe44Fno0D8+Z13gw9zX4ZHz8lee1xi6Dofzn4mfo/bBjRJA1tkKeY/j4LS7ZL/GwbYLAqmyeObbcm9PJ/t/T5zcPJNfR8P3YGMF3HWsmlKpHZEugtPvkfIMwxekH4RM/GIN3/jvx2yO4LJ3c+nUqpAHL9mLPh1amu9shCFMvluieya8t9J1OFxQo0TioIBf+IHcKzCBPY6D0+6uUSIGr7vche8Hz4K5r+kejaBIiTQb34Ota0RP6AxG5XDTcNItMOwU852NwJPOZfecKO1CdVPcTgIr3Yabf9aZEozK0WEAXPg8tOgYD2fjjd9LSbIJ9DtIAsmpjNm2XRjIX49pDkbVZew3pZQqj+TP0fA92LISbj9MeiebQqoAznlUyqkMXZBeEDBjyXrOuu0jtmb1Oxk5OpYU8MQV+1LaxuDMRhhI69CnrzDrwamuw+GiF6W9q6HrDoCXfiLdQkxi8IkSscIxs4wqDCRK9eDp0gLTJA78ERz8U92jaJjcxeU7DldzH6OxuCk49S7Y41hz96vvSSv4O49UX3qxPYrayFnXcXdzDebAh89ekkxGY1skq6BDf7j4VXkXwdS5A3Ew3vi97lF8lX4Hi23npMwMSoUhEIqToaO8cXvsdZlcFM8T+fk2wkAeYrnrOLOcDJAuVw+eJQ+LGfiSoucHLFy9hfPvnGCUkwGwalM1Z976ARsqPfzAICM+R+DB56/CM1eZ5WSAtKq772TzxpUjDOCdv5nnZIBEfp79rplOBkjU+8lLzXMyAN76s9wXMSGzVx+BL9F4k5wMkHE9frG8E2OgniDwYfMKuOd4s5wMkHbE95wAm5aZ+aq978GiCfDYRWY5GSAPT959/NdffzaFwIeJd5rnZADMe0P2rIlOBoj+ev775jkZIO3D374ub2suP9+I40qnmtVz8vLxzSa7RbzK0LBDBnks79sPTWHDVgMPaKBifSU/fGwqKdcwoy8MJDL61GXmKY8ciz6Ct/5kntHne3JZ/o3f6R5Jw0y+W97wMM3oCzyYcr+0ETWVl38mBoxpcwfwyi/lIS8T8avlgrBfZZ7R56bgiUvlZWoT2bxK5s608qkwlO/ziW82/qFM1SyfAa/83LzASuDJQ60v/UT3SBpm1tMw+R7zbADfg9kvSDMkU3nzj/IgZR70RPSOhu/JYy1TH4r8oyNl9Rx47TdGKZAgDLnpzTnMWBLBw0p55NVPVvDklCV4gUEGs+NK73MVLR2bw7s3wPJZhhl9oRgtph3O2/LcNVC9ScqUTCDw5W2M/xmseEEMqicvNSvS52fF8Z5wq+6RbJ8NFfJ+jElGX+BLlkpV2+Sm8sX78OHNZp0rjiOGsmmVFtvy8e3yJoUJd0dyOK7oCRWtf5vD/34mj0Gbsu6CQILbz35X90i2T+DL90v0+jVazZMrmXr225F+bN748CZYMtGIzez5AfNWbuJfrxmaBdqGXz8zk/VbsmaUUAWepCNNuVy1PQJPjD5TCAPpG5+vB5aiZPNKeR/AFIPZTcldoMr1ukeyY5ZMhnf/bk42LQzkjRGDAj0NMuU+mPOqGcGB3Pssr/5K90gax2u/gQ1LzCih8rMw9w3JjppOGMrZYkoJVRDA+zeqb//bFKo2wFNXmHO3ynVFb+XrIb4oWfWZdJyMeM1Fq7FzJVMbl0X6sXkjDODJbyH9M/Xiug7ffbicat8QQ2AHrN+a5UePTzOjhKpqEzz/Pd2jaDzLZ8Lbf9Zv9AUeLJsB7/9D7zh2hhmPwyfP6Tf6Ak9ecv38Fb3j2Bne/COsmWdGpO/VX8lY4sIzV0splW7clDhoqt9naSrZLaJjTSihCrJyfy8urF0Ar/xCfzYt8KVkysR7GQ0x97WaEirNesLPwuwXYfqjesexM7z/r8hLqKJzNMJAFMe0hyP7SCWsnis11hoNF88PeGnGMuNLprbl1U9WMH3xenydpSyBD+//0/ySqW354N/iIOnETcPrvzHD8NwZXv+tAR1ZHDHc44RfLdkrnZG+MJR7BR/frm8MTWFDhZR56TRcfA/mvWV+ydS2fPG+vFWhMzgQeDDhdvNLprZl4n8lcKszq+Gm4E1Fr6VHyZt/0j0Cabf72m90j2LnCAN4LVodG2FGw6lJzevPDuw0H9yo1XBJp1xueztG0b063PzWHFI6S1n8rHTBiBvVm+WtCl1GfhhIp585r+qR3xxWfiqZBF2Gi5+V/vsmvA20s8x6SoxmXed0GIqTbUC56k7z0a16k9+pNLwXo+xjXd77h/7gwEe36JXfFAJPIsy6Fl4YSsnPjCf0yG8OG5bA9Mf1nTWBJw72ill65DeHua/BytmR2SfRWYjZLTD9kcg+Timr50ikSEO0KghCZlWsp3zROuWyo+B/M5ezerOm7h1+VtacCQ9VNYWJd+pNi0+4NZ6BAZCx6zJcUhn4+D96ZDeX3EViXWV7YQBT7tEju7lsXFrT+UyD4RKG4tia8hjkzjLvTXm9XMd543tSvrJhiXrZUTDlPn2ZtDCQ7KPuEqSm8vFtcl7rwE2b8/BtU/jolsjsk2gcDT8rNWjZrZF8nBam3KOnjtSBBycsUi83Ivwg5NGJi/F03C1JZeQQjisblsD8t/Uc4oEP0x9TLzcq5r4ul8NVE4aw9gt5XyGuTHtIj4PrZ6XF45Y16mVHRfl9egyXMIjHJebtMfkePQ5uKg1T7lUvNyoq19XcS9Pg4DoOlD+oXm5ULJ4oJf06HNwta+J1h29bZjwRmW0SjaORysQztVaXT5/XUorhOg7PTatQLjdKnptWoeel8E0rpEVmnJn+mLxkqpLAF0M9rpkgkN9hhoa0eODFN3ObY0OFPFimWvmmMma/N9IY5r0h7/Woxk3FX8fOeFzP/aDqzWY+prkzzHhcvYMbhtKtbn18A6EATHtEfYmyn4WZ0RnqWqhcJw8gRzB30VmHFVMi+ygtZLdqafFZsW4ra7fEsF65Dp8u3Ui1pzhSFfjxdzIAlkxSH10OQ2nrHHeWTFKvfFMZkRt3Fn8sXXhUE/e5C3zRdaqdtMoN8erSVR/rvoCt69TKDEOoKI+3wQd69k2QTY6OVV1m66bjf9ZBze/Q/LMuGkdj7RfSuzjuKH5Tww/C2N7NqIsXhHy2XHWUr0aBxJ1Vs9W/UJtKJ2PudP0OiZi7KZAqUCuzcn38o6MgUV6VhmsYSrvJJFAxRW35VJCFisnq5OWLjUvVd1ZMFSRj3en4HRwnGXpi6ZRIrhQ039EIvHg84tIYKsoVp3ZDpi+OwWNfjaB80TqyKu9puGnZBHEn8OVdDdUkQYGsnqP+XtiW1aL0447q7z8MxEBPAkvL1WbSAi85c1cxWW0ZS6og/tUWOZZMlofzVJIEY3nTCtik+D6fVyVBxLizdGokHxNBRsOJbDDaWTpVHh1URMp1mb4kGY7GjCXrSat+vC8p627JJLWPgW1eJYdv3AkDWDZdXRlLEMCShBgta+ZB9RZ18nIlR0lA9bmTyiTnrKsoV1/umJi5mwKhQictu1WCOUmgYpJaB3fFrPi9T1Ufm1dF8qJ5861qNyXPlicBDR7ovJWaH22LiDkrNuGovGtQtVE2QRJYNVttxzMNd5HyxopP1JWxhB6sSsjchSGsmatOXiqTHD2xZp76JgRJmTvVv0cQqF3n+WTVbLVO2toF+tpgR02Eb0LsEN+L59sZDbH8k2Z/RDTh+6zCyFg+0dCed0s2AV4vsFX175GUNQey7hRm0rS/SB4l2S1qL+aqzALkm+rNauUlac+qzEBCcuZOtY71q+L7VtC2qJ471edDPsluAWVx0CBZeiK7qdl7KJowqurLrPkiDOQvhUafim5NPzhyd87du3deZaiumsJTrOjzSeDJRlaREQoCUb5JIfRR92quk6y5Ux2VT4qeAPVlEUmZO9X7J44v0DeE8v1aqVZePlG5X0OSpSeCnF3c9PvL0TgaOnpjJ4SUAuPy3TmrWL81v4dU51aFfHP/fnmV8RWStuZUlZ05jtrsSb4JQ9SFqkL1b54kiaTtWZUkZe5U758knXWq26An6qxTqCcc7NxtQzSORrooko/RTiqj/GAqzLhszLPz+8Hc1XwwN7+t8Ub0aKPW0UgXqpOVb9JF6jIajgOZ4vzLUUW6SKmfkZizDtSvgyTNnerWwEk571T/HmnF31M+Ub1/kqYnVGa+k3TWpYvAbZ5dHI1V3aJDJB+jnRYdlYts3zIZB2H7looVSGGr5ET5WnRU202kpIs6WfmmZUd1wQHHEXlJoWUndbLCIDl6oqAlZBQbEhp0U15QvX9SBaIrkkCLjmovZyfprGvREXUZDTdZcxeBvdB8De1XQ7cRzf4YI9DwewwpbaNcZj4Y2r01nsp3NFIF0GkPdfLySbcRKLypJvOmusVkvug+Rl3HrlQGuo9WIyvfFLaCdvm9t/UVAj85eqLrMLXyEjV3w9V3Muo6XK28fNFthNq7Bm16QFEy7BO6j1an89xUcvSEm4LOg5r/Mc3/hAQp39IyaU2miKwfMKx7Mjby8B5t1La3DUPoVqZOXj7pMUZtdiaVgU7NPzy0U9QG2vZUK7Pj7urLZvKBauMrlYEeY9XKzBfdyhRfLg1FNyWB0jK1cxf4yZm7HmPUB4iS4OCmMpEYyztF61IobqdWZj7otEck5Y7NdzQcRw5e1ReV8kHpKKV3NNKuw4iebZXJyydlPduRUtl6KsgmQ4EUt5NDSSVJMVx0KMFUGroMUS83alQbfAAdBiSjdrm0TG3L1FQ6OU5a97GKjeWEBKRSBRLkUEngJ2PuOg3Sk8EvHaleZtR0K4vkrIvGqi5oAe0VXgTOF91HN/vSy87gOA5DSlurbw0bMR1aFtCpleI7GqkCKZuJOzoOoyCbDAWiOrIMUvaRlLlTdjmyBjcFXYeqlZkPeowV418l7fpBpoVamVGTLoKOu6mV6SbESesyRP2aIyEBKdWBAZCW9UnQE6VlYi80k2is6jCAvgdF8lHa6DhQywWeokyKsphnNfYdoOniU9dhUNRWj+yo6Hugupetc6QKYMChamXmg/4Ho9xYDgLod5BamVHjuPI7qHyNHsQp7HuQWplR06qbnqCa60Kf/dTLjZLe++pp4NG+L7Turl5ulPQ9UH1QxU3XnBMxb7rS9yD1OhanRj/FnP6HRFIqHJGjEcLIcyP5KG2Una1hMYLnB5w6uodyuVFy2pgeeIHiC34gB+DQU9TLjQrHlXWn2uADaNcn3pG+Vt30GMupNOxxbLzrb/sdDCWd1ct1XBh1nnq5UTL8DD0vTftZKDtHvdwoKTtHzwN6gQ8jzlQvN0pGnaenPL1FB+gf46BUURsYdJz60ik3JYGBNjG27bqPknLXCIjG0XBTMijVF26iwk2Lo6TB4EunXE4s606LgnhGDUrbFLFv/46kFZacfUkYwugL1cuNigGH6TH4QBT+qPP1yI6CsnP0GHwg592w0/TIjoJR5+sx+BxHHNxe49TLjgLHkfNGxyNwqYw4uHFtEVzcDgYfr6dW3nFrvreY1ij3GCsGn45153sw6gL1cqNi6Kn6mneEoQQS48rI6PREdCvXz8LeV0T2cUoZerLanvLbUFyQ4rQxirvnRMSF+/YlUF2+ksNNQbfh8TVc9vm20i5nXyGVkShfHPt9pwpg78v0vfobAvtcHc9Xh9v2gkGaDD6Q9T7uKj2ym8uAw6UMR5fB6rgw9mI9spvLmIv1vZbsOLLuBx6lR35zGXeVRj2Rhj2OgXZ99chvDo4j57RO+2Svy+L52GaLDuIkRaQnotOUqQyMOAtadY3sI5XgOLDfterrH7fhWwf0I5OKV8SldXGac/fupSebkcP35PuLG93KoO/+Gi741cGpOQjjxvDTJTCgy+Bz3RqD/QQ98pvD3legTfFCreESUUpeKftfq6W89kvclHx/cbsUnimGcVforfUPYqon2veDwSfo1RNBAPvEMDiw+zE1gQGN9klxe7GL48ael0Ra4RPtN+A4cOz1kX5k3tnzUin50ngIuo5D1zZFXHFQvJTvL44dTGFac8lXKg0Dj5BDJS64KTjhn/qiVHXHse931HeCaQ4tOsDhv9MeGCDw4eg/QWFrvePYGbqNkPNOx52gugQBHP/PeJWyDD9DMqe6566wNRz6C71j2FkO/pn+ph1uGnruGb9SluP+LvtFJ6k0jPmGtP+PCwUlcMx1+vVEGMJhv45X5UCH/uKUR2gTR+touGnY47j4XNBt3w8O/62+Wu86uI7Dtw/djcHd4mG4HLR7J04b01Pt2xkNEfhw4o3xuaC7z3ekY5bOKNWXuDD+tviUAR17PRS10t8JxU1By85wxO/0jqOxpArg5NuMOOtIpaHPvlJOEwdadZV1p/pF6/rIlWP03kf3SBpHz71g3JX69yvI93f0derfLWoqoy+CfgeaoSfCQM6PuJQBHf4bKOmqf925LhS2gmNv0DuOxuK4MP7WyMsco7cuggCOu0HfJdfG4jgw/haZWEMia2EYcsMZZcaXULUuTvPXU0fgBwYYLSCHSVEbOOavukeyYzoPgoN/ao5hn0rLWx77XK17JDtm8IkwZLz+qHIONwWjL4hHV5YDfygtvE0wWkAcniN+L5fDTef4f0G62Jw9GwYSHDC9hCpTXOPcGuCggXx/6SI44UbdI9kxbXvBUX8wIzAAcuZ26A8H/lj3SHZMv4PkLpNuJyOHm5bytyEn6x7Jjtn7CnmfLGI9Ef3J6bqQaQnH/yPyj46UvS6TaIuuS5H1kE657NalhCsPNruE6pfHDaZdywIzshk53DQMO1UuupqKm5JoAQbNG4ijfcjPoZPil2d3hhYd5EwxxWjJEfhw0k1ml1CVjpRUuCmGMsiac9Nw0s3GBHrqZcRZUpppioMGMm+tS+GwX+keyfY55OfQpqc5gQGQ73HAoWa343ccOPEmcDNm7Q3Hhf2+Kx1GTaWwlZwpukumtiWsCcBrbDq0QzoMgEN/mZc1lx/Nk0pLzfzBP8vLxzebAYcZW/KQK6E6bng33UOpl2/u35dTRxtSMrUtQSCGvIkvcjoOnPBvg0qmtsWFcx6Dki66B/J1ClrCOY+KEjHJWIaaEqqOcOYDZpYVtOkJZz2E1gvgDZFKy72Ho/+ieyT102ucmc4t1JZQmdree+R50i3JlKhyXcIQjv2bPCBoIkf+Ud5gMCgI+iVhAGc9DG176x7J10kVwBn3iQ4zbd05LhSWiB4rKNE9mq/TshOc+0TeOsPlV2sf+ENp4WkSvfcRoyDPv3pz+ceZIzl4d7PKz84c25OfHztY9zAaxnXF2Dv/afOi80f9RdrJmmYo50iloVUpXPCsWXdd0kVw5oNykdmkyGhd3LScK6fdZZZxUNIFLnweWnQ0d+4cRy6oH/pL3SP5Kt1GwLmPy7yZumfDUKKkpt2JHDJeml2YUvazLbls2jmPmnfB+eCfwt6Xm5XJqIubFv1w4fPyaKopuGk49U7os795TkYONy2BxrMflrJCUyhuB+c/A6275y0I6oShgtPg9d/C2wbUz/c/FM56UL5wUxdjDUEQEoQhVz84hRdnLNM9HC7cpw+/PmEIYRjimHoI5vA9qN4I95wIS6fqHYvjijFgauRxWwIPVs+De46HjZrXXUEJnP0I9Nrb+P0KSEZtzqvwyHngVeodS9teNcZAqaEZtHp4/1/w8s91j0JKas99AjJF5jpoOXLZlmeuhin36R0LSKnZSTfJ35vqoOUIPPCq4L5TYOEHukcDh/2flCbFAd+DTcvgrmNh7QK9Y0kXwun3wG5HmL/mQMq6Fk+A+0+Dqo16x1LSWYKLHQbk9axT42gAfHy7KJHsViXivsaoCyRd6jjxMFqAIAwhhN+/8Al3vjdfS4CoIOVy7eEDueyg/uqFN4ecEnn8mzD7BT1jKG4HJ/xLXvSNwwGYI6dEHjoHlpbrGUO7vnD63dBliPnGXl0CH5ZMhMe+AesX6xlDz73gjHulh7tJGZbGMO0ReP5afQp46ClS4x2DYNSXhKHotTf+AO/8VU99upuC/a6BQ35RO544EPiiK565StaeDnKtWOPWetf3oHItPHyePketdSmccqe0Lo7LfgVZcys+laDUmnl6xtBthJSaKQhGqXM0Ah/WL4InLoVFHykRCUg66MQbof8h8ToAt2HigjV879GpfLF6izKZQ7u35u9nlNGvYwmuiXcydkTgy+Ez7RF48Yewda062bsfLd1NitvGy1DOEXiAA+/+Dd76C/jVauQ6Doy9RNpOu6n4GcogCtivgpd+DJPvUSc3Uyz34sZdKZHuWK47HzavgKeugLmvq5PbspNkHgcdL5kpnY+QNpUwgGUz4MlLYcUn6uR22l26YHUbHq+ASo7c9/3p8/Dcd2HTCnWy+x4I4282oxVrUwg8+c4/ugVe+43aQPLIc+V+V6owPlnbuvgehD68+iuZP1WR5FQGDvgB7P99kalg7tQ5GlC7KD+8CV7/Xf4X5cjz4Og/x3ch1sHzA/ww5E8vfspd7y/I65rMpByuPmQ3rjx4AGEYkk7FUHnUJfCgcj08fVX+sxvF7eCoP8OIM2odnTgTBrDqcwkQ5Du70a6vKN1e4/IrRwW5oMbc12XdbViSX3k994ST/1PT5Sfmay63bybdJVnwfGc3hp4i2e6CktjrCXkENIQ3/wjv/T2/2Q03JXcwD/4Z4CRj7rKbJaM2/bH8yiookbcexl6cDD0R+HLGPXFp/rMbrUsliDfg0FgHj7/Coo/gycvyn93oNkKCAp0GKg0KqHU0cgQ+bKiAd66HqQ9GW8/sONJVav/vS213UhZiHaYvWc+Nr8/hlVnLiPIpi4KUywllpVx58AB6d2iBm6R5yx3mn70E794ACz+M9vMLW8s9jP2+K296xDGa3BC57MaU++CDf4njESWtu0sHndyr1XE3WOrie5IN+vAmmHAbbFoe7ed3HizG3ogz4pvFaIjAhy2r4J2/ydqr3hTt5/c9QNr+9j84vlmMhggDWPkZvHMdzHyqZg9HhJuCwSeJju28RzyzGA2RWwfz3pJs7rw3o/38gpYSid/vGnnwM+4ORl1ygeRpj8L7/4TlM6L9/JadREeMuzIRweOvkMtuTLhNshtRl912GCBd4EadJw0IFc+dHkcDai6xOVC5DibfC+X3w8pPm/55LTvB8DNgzEUyqYGXLKVbBz8ISbkOi9du4YGPFvLE5CUs29B0Z61PhxacOronZ+7Zk44lhV9+fiLxs5I6rCiXiOnMJyTb0VS6j4Kyc+USZLrQqAcgI8f35ICa85qUBM1+oeklVY4rBl7ZeTC45u2ThO5XQIzmMICZT4rRvODtpqfKM8Vy72fUBWIs59Z0EglDIITqzaIjptwPy6Y1/fOK28l7O6O/AV0G167pJJILrmxcKmdd+YOw7oumf17bXtI5b/RFElVOQiS+IXLrYsUnMOm/MP1R2LKm6Z/XdZjcwSg7V9qcQrIctLrkzqMF78Lku+GTZ5teveI40kmq7BwYerK0X03qmoPaoN4nz0H5vZIRb2pWMlUgJdwjz4PdDtd61ulzNOqSm4A18+U2fkW5lGksnVZ/FCuVgU6DoLRM3kzoMQa6DOXLh9CSFJnaDmEYEoTyW89evpEpC9cxY8l6ZlSsZ/ayjVR5X+//XpxJMbi0NUO7t2FY99aM7NmO/p1L8IKA9C4yb4BsXseFICvrbckkWXMV5bDqs/p75xe3k9RjtzJZez32hDbdk23o1UfOia/aJBefl0yW7l5LyxvuQNKqW81+HSEtJXvuKfO5q81d7vfdvAoWfwwVk2vPu/pqwx1HSspya650FHQfDQUtEh1MqZfc3K1bCIsm1OiIqfJXfcECNwWd9pC56zZCXrztNlz+fcguoycIQ4mWujWG8+KJNXNXLnc66qsoSBdJI4bSkbU6tvOg2nMzqcGUbcnpgSAQB3fJxJr9OlUCo/Vligpb15xzZfL/PfcSJy3JTm195M6n7BZYPAkqptTo2Cmwdn79gZaWnWrtutKRMnctO+66emLrWlhUoydy592Givp/pm3vr+rYHmPk7SkD1p0ZjkZdfK+2M1QYwNZ1chB6VTLx6SIpTUllZKEG3q61ALdD1g9IuQ6u4+AFAeu3ZKn2ArJ+SEHaoSCdom1xBtd1vmyfG/v7F1EQhuJwpArkn70qqNoA2UpZX+kiaXWZe18i8IFw1zLyGiIMZD5ye7B6i7QW9qrkv6WLINMCimpezvazNVEpu+6+to4qN4hS9irFmEsXiqLItJD/7mdr3nXYRYy87ZGL/OWim1vWyJrzKmv0RGGNnij4+v7e1fGzMm+OK2swp2P9apmjdFFNE4vU1/f3ro5fXftit18tDq5XJXOaLpJ116K9/NnAV3bZ1ni2tdWyW+XulVfZgJ6oYwfu6gSBBApyc1e1UTK8XpX8c6ZI7vwUtJR/rru/DcE8R8NisVgsFovFYrHEHnNcHovFYrFYLBaLxZIYrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIsc6GhaLxWKxWCwWiyVyrKNhsVgsFovFYrFYIic+jkYQQBjqHkX8CEM7d00lN3eWnScM5C/LzhPa/dpk7FnXNKyeaDpWTzSdMLR6oqnESMemdQ/gKwSe/L9bM6wNS2D1XKjeDH41uCnItITWpdBhgPxzGEDgQyqjb9wm4GfBSYHryqG3dh6sWwTZLfLf0oWQaQHt+0LbXvIzgS8bPWXWMlCOn5U15zjy9ytnw6blMndhAKlCKCyBjrtDSaean/Hkz7spvWPXSRhCkIVUgfxzdgss/wS2rgGvUv57ugiK2kDnQVDUWv6cn5V5c+IT54icbc+tyvWw4hP5f69K1la6CFp2hE6DIFMkf86vBjcj/31XJfCBsFZPbFwGqz6v0RNVsrbSxdC6G3QcKH/O6glh23Nr7QJYswCym2VfpjKiY9v1lr8c1+qJHHXPrcCTNbehArytNWurEApaQscB0Kqb/EzgAVZPfEVPeFVy1m1ZBdmtNXqiEApbQefBUNxW/lxdm2ZXZdtzq2ojLJ9Voycq5d9liqG4HXQZLDYeGKcn9J8cgS+bMLsVPn8ZlkyGpeWwdCpsXdvwz6WLoOtQ6FYGpWWw2xFQ0qX2sNwV8D05/Lesgc9eknmrKIdl08Xoa4iiNtB1uMxb6UjY7UgxpAOvVnknndw6WfsFzHm1du5WfiL/rSFKOsua6zYCeoyB/ofWOLzsOgdibp0smwYL3pF5W1oOq+dsPyLatresuW5l0Gsc9NpL/rzjGnMg5pVc9M5xYMF7sOjD2rlbv7jhn3NcMZhzc9f3QFEquf2/KxD4Mg9+tezXJZNh6RSZvy2rG/65VAF0GVKrJwYcDm2671p6IrdfK9fDZ/+Diimy5pZNF8OlIQpKoNvwOjr2SDECd6V1l1snGyrg81dq9cSKmWIwN0SLDrXzVjoKdjtcPicMdx2nIzd3Kz+FeW/Wzt2qz7YfiW/To1bH9tob+uy3C+oJX/bsogmw8INaPbF2QcM/5zjQvn+tnuizv/y9AXadE4aacqW5w2rJZJhwK3zyrESkmorjQr+DYPSFMOh4WchJNZpzc/fZSzDxTpjzWm02qCmki2CPY2HsN6H3PslWwoEnG3nawzD5btnIzaG4HQw9Bfb6lhiDSVXCOcVQtQkm/Rem3CcKozm06QkjzoQ9LxUHLhd0SBq532tDBUy4DaY+BBuXNu8zuwyBkefCqAskooWTTCWc20/LZ8JHt8LMJ6FqQ9M/z3Gg1z4yb8NOkX+XWD1Rc47PexM+vkP0hV/d9M9LZcTZGHsx9D8k+XoCYMaToie+eLd5ZWWFrWDwSaInug5LsJ6oCab4VTD5HtETy6Y37zNLutToiW9JkCDpemLzStET5Q/C+kXN+8yOu0HZuTDmGxJMBi2VBOodjTCUw37em/DG75tv6NVH296w77dlcpPkcOQW4pT74d2/SQQ5akpHwoE/gt2Prom+JiRK73tACO//Ez66BTatiF7GgEPhkF9KFDAp8waidKs2wZt/EMXRnIBAfaQy4qwd8kto1TVZSiTwpYTx9d/ArKdqyn4ipLA1jL4ADvyxBAySYrzk9MSij+D138H8t6OX0boU9r4C9r5cMpJJmbucnpjxJLxznThpUdN5MOz/fXHWkmT45UrLProFPvi3lG9HTZ/94eCfQe9xtes8CfieOBhv/UWCUZXro/18NwWDThA90a5PsioIAl/KtV//LUx/rHkBgfrItJDA1ME/FadXsU2s1tHILcQXfgDl9+dfXo+xcPJ/5E5C3A/CwIdNy+DJy2H+W/mXN2Q8HPd38YLj7qiFgZTiPfktuX+RT9wUjLsKDvk54MbbeAkCOcxnPQPPXwObV+VXXkFLOPw3klmLu/ESeOJsfvBvMZRz9bT5olU3OPFGGHBY/I0X35P5e+UX8PF/8n9Buesw0ROddo9/gCDwYOs6ePpKyWDkm92OgJNuksxuEvTEqs/giUtFX+QTx4ExF8MRv5N5i3NmKHfezH0dnr4qP85ZXdJFYjDvc3X8A8k5PTfxTnj5F1C9Kb/yWnSAY/8GQ05SGkhW42jkFuKc1+CZq6SEQBXpIjH6xl0Zz0WpeiHWpWUnOO6G2lK0uCnhXBbjjd9LJiPqaPL26LQ7jL8tvtmNwJPMxXPXwIzH1cruewCMvwVKYprdCHxJeT9xqUTkVVJ2DhxznVxMjauTu/BDePIyWDtfncxURiL0B/yAr1w0jws5PTH1YXjpR9u/3xg1RW3hqD9B2Vm1wYk4kctivHM9vP2X7d/Ri5p2feCkm6VkOY7kgscv/VhKpVQS90By4MPmFRI8nveGWtmKA8n5dzRytd0v/AA+vj2vorZLr3Fw1kNywS0uCjjw5MLZI+eJk6aLEWfCCf+OV5clPyupyPtPlQ4XOnBTcNBPaoyXGBH4sPhjePhcqRfVQUEJnPhvGHxC/By18gfg+WulwYUOWpfKWddlSHwM5lww6tX/g/du0NdmtVsZnPOoROjjEmUOfNEVT1wCs57WN449joNT75Q1Fxs94UHlWrj/dKiYrGcMjgP7fFuyuXHKRgYerPgUHjxj+80s8km6CI75K4w6T4/8phIG8OkL8NRl22/IkE9adoTT74Wee+V9v+bX0QgDiXA8/g29B2COTrvDBc9CcQfznY1cXfw9J0q3Ad0MOBTOfDAeSsT3JBp693HS+lI3e10GR/9Z9ygaRxDA3NfEudVlKOdwXMmojb5Q7zh2hvf/BS//XPcopA73rIela4vp+zUMgRCeuVruAOmmbW+48DkpRzPd2cgFo+4/Fb54X/doZL2d+4S0KzXdyfU9KUe+69jtd/NRxYizpAwtDo0dcsGo+09rXnOGqDj8N7Dvd3SPovFMuRee/Y7aKov6SBfB6fdIV7Q8BvTy52jkug88fA7MfjEvIppEu77wzVcl3WuqsxF4UL0F7jwSVszSPZpa+uwP5z1R8+aEoVFmPwvrvoA7jth+20vVjLkYjvub7lFsn8CXOtuHzlJbPrAjjvu7XHg2dc3leO+fcq/AFNJFcN6T0HNP842+J78l3bhMoVU30RMlXcx1NgJfLo3efRwsnqh7NLV0HwUXPi/le6Y6ub4Hm5fD7YepLeXeEUNPgVNuN/usCzxYMkmCoLqDUXU59Few/7W6R7F9wkCCKc9crXsktbhpOPN+af2dp/2aRxfGkbSQSU4G1Ea6s1vMfM0zDMXIu/cks5wMkDcTHjnf3NdjA09qHu8+3iwnA2DiHVIWYiqBJxGqR84zy8kAKUOa+aT+6E9DhAFMutssJwPkAvoDp0vXIb8Z7a/zzQvfN8vJAGk/fNex0jnH5HX34JlmORkgLevvP13eAjCRwIeq9XDXcWY5GSD34Z7/nu5RNIyflXKp+041y8kAeO3/pI2zsfaJL41VnjUs8xJ48MgFcp+wOc8kbIf8OBqBL+3hpj2Sl49vNis+gaevMPPSmuOI4l0ySfdI6mf2i/DOX7f/4I4uHFfuFZimPHK8+zeZP9OMviCQMr2HzzVPeYCstaeugHUL83YQNhnfk/PkeUMjaVUb4aFz5MKmaQo48OTy8oT/6B5J/axdAI9dZGZUPgzFsJr3pu6R1M+Cd+CVX+oeRf24KXjsYlgzT/dI6mfinXLPy7SzLgxlTA+dbUa5VH28+AN5t8M4HevJPZanLjPTdvIqJchYtTEv44ve0g48aW9mcvQW5IHAGY+btZl9Ty59m1CnvD3evk7axJq0mcMA3vuHRNNM5tnvmJdNc10xlHVd/G4MXiU8eal5JQUO0l3KpHNkW9Yvgv/91Ky678CXNqwv/Uj3SLbP/LeliYlJWQ2/pnTlg3/rHsn2+ehWeSfLJD0ReDDxv+q7/OwsL/0Ytqwxa905jtw/W/eF7pE0TOCLnsCwoIrjyrhMDOTl2LwKnv1uXnRsHj4xLfW22S2Rf3TkvPB9qNxghtEXhhJ1fOYq3SPZMX5WjCtT7Bbfg9Vz4c0/6h7Jjtm0HF74njnZtMCrdbpNZ9EEMa5MUb5hAG/9GZbP0D2SHTPpLol+m2L0uSk561S2YW0qr/xSSqmMWXe+6FgTI6N1CYOacRoyb4EvD7WaVuJYH5Xra6ouDMmm+R4seFdKgE1nxSdiC5iyPwIfPrxZ2nabzqynYOZTkQfOorV2chNqQveLxrBljVzKMcHocxx48Yfmlv1sy7Jp8LYhJVRuTbTAq9I9ksYx7REzSqjCmpKp567RO46d4fXfSURNdwbB92D5LHjH8Av+dXn6Sglm6CZXMmXa/b2GqN4s73qYYPSFIbz2G1g9R/dIGseaefDqr3SPQnBT8NTl+tqJ7iyfvwJT7td/1gEEWSlfNa38siHe+7sZJVSBJxnl13+rdxw7w/PXyh6JMAAfrYWd3SqPo8WJT5+D+e/oXZCBD8tmmF8ytS3vXC+XrnUePn5W6llNL5nalhd/aIaD++YfzC6Z2havEl76if4uSqm0fIcmGAGNZf1iuSekOzIfBvDyz/SOYWdZ8I5c5NSqJwIxWj68Sd8YmsJHt8p9F51BKT8Lnz5v7p2WhnjlF/r3a+CL4W5yydS2BL683aa7s6iblrJVk0umtmXLagnoRVhqG52lE3jw8X/iEy2oy3s36F2QbkoMgLjhV0spi04FksrI2wVxY91CSVHq7PBUtSl+zi3A5/+TUjld6y7woaIcvnhPj/zm8PEdetec70lgIE7ObY73/q5XTziO3EMzIYu8M4QBvPt3vWNIZeT7ixtbVsubCzr3bOCZ27Bheyz6SDqy6XLUwkAc7Nkv6JHfHMrvl/K9iIjO0XBcmHBbZB+nlDmvSYpXV2R+80ozHjRsChP/q68GN/AkQrXyUz3ym8uHN+nr0R94MOm/UhYSN8Kw5iKspktCbgo+uFGP7OaydS1MfUCf4ZJKS3ltHFkySTKnugyX7BZx0uLI1Af1nTVBAEunyh2vOPLhzfr0hJ+FaQ+b1y6+sXzwb70ljx/cGJ9ys7pkt0r3s4gy9tE4GoEHc9+Mz/2C+ph0t55IUeBJVDlOJRh1qVwHs57VY7i4aZh8j3q5UbH445rIvIaDyE3HM5uRY8ZjUjesg+rNcoE+rky5V4/hkssExTUwAPD/7Z13nB5VuYCfmfm+rcmWlE12N50E0rNpkIahdxKSUKWrgAgiIBcvKqJevHitqAgKogIighI6UpUizYRkEwKBkAAhZdN72ex+M3P/ePdzl81uyu7MOTOT8/x+uWouzHv27Dnnrec9c+7R0/nMrZe7XXFostISmVoxWHXoCQvR73Fl/WJ9kXknLedFXPngKWn4owPPhXf+pkd2EFT/KbAS5WBOTDsF8+4P5FPamP+gHs/XTkXvsar9Zf4DegyX+h1Sdxtnqu9X7+D6vkT41i1SKzdIajfDwqfU18y79dKhK1OrVm6QrJgDGz5WL9eyRHnFGV0PRzppmB9zPTFPk57wPXh3pnq5QVJ9vx4Hd9On8c0EgTSIWfA39Q6um4FFz8Sjq15rrF8iuiKAQGhwK3fpG4F9Sgtba+SypGpqt8Q7wgfw6VvqZfq+bII4G3wgHdpUO7hevVxujTufvqZe+TrpeN7NaM4nr6pXvpYdfz1RuxnWLlQv162P7iOu+8rKuXo6A65dFG+DD0RPqH4Hx62Xd2TiztLX1Tu4lpUQPfGvQKptgtHSOzfKI31xZ/kstSVMvgcrY9YtqSV2bYGNijtSeJn4dZpqiVXz1Wc0nBwpYYk7K6v1dO5KzNwpdnDduvgHVUDKWFQ7aWvf13shOAg8F9a8p1amWw8rZquVGQbrFkG94qCa5UBNtVqZYaDjZ7CdZOiJmupAnLT2a2nfS4bBB+oXhudKlCcJqHbSnHQyDsG67dKZQjVJmLvVC9Q/tllfG++Ssyw1c9Vng1a/F9+7aE2pqVbbXtmtF+cmCax4WxxOVSTF4PM99Q+D2nYy5m79YvV3m3xPgohxJyD7tP2axnOTEZUH9QrESSdjI0OD4ao4tZskJ03lXYP6HfF58GtP1O+EDYp/jtUL4tdetCVWv6vW6HfrkhFZBjmzVZax2AmJLENDJk1hGYtli1OdBFbMVuukeRqcmzDwfah5R23TlY2fxLOjY3M2fhzIz9F+R8NJy2NzSWDVO+plrtYgMwxWLVBbilFfK5sgCaxaoNZwWb0wni33WmLlXHVOmluXHIMvs0vthXA7QXpizXtqnU3L1qObwmC14rMOxKlOAqsWSNmrKjZ9Eq+H5vZETbW6LoVJqlTx/UD2TzC58wAf9tCKjp/DzF3bqNuWHGO5drNaJ23nBnWywqZ2M6DI6PNJzn4F2LlJnSzLSs7cuXXqLzUnZe5U/xyZXXouoIeB6rlTeT6Ejcq5873k7FeAHe23F4JxNOLe+SeLl1Ff852UQ1D1GkjKvIHaufO95OxXkHWgyt+0SNjcKY5WJmnuVJawQHLOO9URctW/pzBRrmMTtF8zu1BW2u37CZu72nbbxQHdBkxIZBnUd7FJSlReOQmaN8tSuw6StOYsC6WeRqLmTnXHrgTNnfJX6ZM0d4Y2oVpPJAmV5XoWyfo9WTbtPX+C0TSpvEA+ox0dD/alctXLDAPVP0dS5g2k7lbVQWjZydmvILX/ypSIn6y5U1nvDQmbO4VNQyA5553qNaB6jYeJSj0ByVlz0NCiVaHxn7Szrp1BqWAcjZwOgXxGOzkd1cvM1SAzDFSvgXShWnlhktNB7WvDecXqZIVNbgeURpdzE3LWgfqzJyl6wnbAUWyE6dBNYaB6/6Ry1XaSDBPV+ye3SK28MFE5d5adMD1R3G4Ht/2OhpuBskHt/kwk0PFzJGnuVN5vySmA4h7q5IVJ2WC1XWySsuYAug9X9+qrnYZuQ9XIChs7BZ0PUifPrUvOuutysPrsd1LmTsfP0XWgeplhUDZY7aONpX3Uv6gdFt2GqmurbKdELyWFbkPa/Yn2OxqWBRUj2/2ZSFBRpTay7NZDeZU6eWFSUQW+wrmD5MxdjzFqD/S8IijprU5eWDhptUaEZSVnzXUdqLasxE5D5Wh18sJE9Rpw6+R8TQLlVWovaPt+cuaucpT6d77KBquTFyYVI9WWnXUZkIzSs+IekF/S7s+039GwHTGUkkB5ldpLPJaTHCetx1i1xrJbnwwFksqVQ0k1SZi7ssHqI265HaBTP7Uyw6CiSvFZZ0H5cPVvKIRBRZVaY9nJgcqE6NjK0Wof7PMSFMwrr1K7f3w/GXNX0luCayqxU4FkArQT0O8/mDsaHbpBYZdAPqWVHmPVXvKz7WRE+dL50Km/Wpm2AxWj1MoMg25D1NcQJyWTpjow0FRu3CmvUveAVZZ0AXRWfE6EQeUY9ZeMuw3V0CUsYCxLSkpUGstOTjICoZ36qa/79zLJCEjp+Bl8D8oTEESuqAqkXC+4k6vnYYF9Sgv5pdCpr3q5hV2gpJd6uUFSMUp9W2DLFgWio1NYkPQ4VO39DJA56zVercww6DlWlKFK3HroeahamWHQe7yejjxx1xOpPOg+TL3ctCa5QdJ1kNytU03ZYHFy40yPseplOmnoPUG93KDpMVbt3RaQEvy4n3UAPcdJ5U07CcY6dOth+FmBfEobQ2fokeu5MOxMPbKDYvhZ6jcySPek/seolxskVZ9X3yLfsqHXYfG+TJ/OhyHT1ZdOOWkYfma8O9l0PUTPpXbPheFnq5cbJINO0VN77WZg2Bnq5QbJ8LPUBwZAfl+DTlUvN0hGnKNn7roOjHcJkO3AiLP16InBp8bbwS2qgD4TAwkiB+NoOGk45CTJCsSVURfqeRPJsmH0hfGtXU7nw7DT9XSncDMw8nz1coOibBCUj1CfDQIpORpxjnq5QTHwFMjR1OK4oDMMOFaP7CCoOk/2jmpsB/oeHu8M7sgL9Bh8Tgqqzo2vg2s7MFLT+L0MjLpAvdygKKqAfpP1zJ1bL+surhx0FBR21SM7XQCDp+qRHQQjzgmsNDk4C8eyYfTFgX1OKT0PlYuKOgw+yxLFG9fI/IjP6/PanZQ4uMU99chvL4depsfgA9mvh14a3/aF46/UY/CB/M4Ou1yP7PaSLoDRF6l/cC6Ll4Gxl+iR3V66HqLP4AMo6ARDpumR3V4Gn6bP4LNT0GdSfFsEj71EfXltFictQdi4voFz2Ff06VjPhfFX6JHdXuyU2AcB3QsL1tEYf0U8X+KccJWe0p8sXgYmXqNPfluxbJj4NT0Xcv+DD+O+rFF+GynsIhE+XQafZUGHMn0lg+2h13i5pKbL4HNSYnDGsWZ+5Ll6H5OyUzD2i/F8DGz8lfqMFhDDZVIM9QTIuFW2jm+OWw/jv6pPflvJ6QCHXqI3k5VTAKNiWDlQNgj6H6VPx9qO6Ig+k/TIbw9DpkHH7oFV2gToaFhSUjAhZpu51zgYeLLeyK6dklq4AcfpG0NbGPMFKO2tJxOUxU5JxCduLUeP/HYgl6zahefCUd/RV4LUFiwbjrtZr8EHYrgc9wO9Y9hf8kth8n/rHoVcqJ58ve5R7B/dhsp9Kl1GC4jh0m2I3BGKE0NniMGls3GHk4YRZ8XvIbXPXQdp3eezBZ+7XjJqceK4H0RAT2REX8WpaU26AI6+KdDAQLAWomXBEd+MT4oynQ/T7tSXlmyK58LUX0Neie6R7BslvWUDac1mNGDZMO238Wn/2O9IGHOx/sPHdiRqcez39Y5jfxh/hbSE1mnwgRgu/SZLGVJcOPFH8viS7n1iO/J71NFJpy3YKZh+p547fM3xPTjpp9JSPg4UdoWTfwZeBHSsj/we41IuWjlaKgZ0BvJA7Lq8IjjpJ3rHsT+MPB/6Hx0BPZGSe5hxyqYd8125FxSgfRLOCp52ZzwurR39Hem8E4Wx2o5EDE64RfdI9o5lwWl3yMNLUbjE7qTlns1hMSihyu0I0+7QW0bQFNuBsV+Cvp/TPZK902WA7NkorDkQJ/uEW+JxR+iQk6LVLcv3YPpdkt2IOod/XYJnuo0WECcxXQBTfql7JPvGqbdKqZ5uYxnk99f1EPjcf+keyd5J5cr+iEIQFOTcGDojHt27iirhxP+LRhAUZM8e9W1Ze1GnzyQ47LLAg6DB734nBd2HwsSrA/90oPQaL4ap7qhyU+yUpOejXkI15otS6hUFxduUY74LnQ/SPYo9c9zNUFgWrXXnuTDtN9G+8JfNWoUUG2kTlgV2jmQio0x+KUy9LTrOLchZV9JLFHCU6T5Myrx0Z4Ga4qTg4BOi31J+6AzpDhcV5xbk93j4dRJljjJHfBNK+0Zr7jwPTv1l9Euopt4GTm50AlIAWA0B+Ajp/ebkFIqODUFPhHN6WjYccUN0o6Qdy+H0u6MTLWiK58ovu1TD44H7QuVoqX2MSrSgKZYDZ9wb3Yumw86QUpuoHTa2Ax26i8EcJYOqKcd8Vx6GjJpzm70Y/rnrdI+kZeyUREbziqO57sZfIdmWKFLQCc64R/coWsb3pCQpqm8cdB0Ip9wajZKp5vi+/F4LOuseScscfDxMvCoaWaCm2LaUUM24O7rlZ5OulZa2UdQT5SOiW6ZsWTDlV2Ibh6AnwlvJlgWff0gM0yhR0AkuehIKu0UrWpDFdsRQvugpqZOLEmWD4PxH5ZCJVLSgAScFZQPh3L/K/ZsocciJ4kBG0UEDWXeDp8ApP9c9kt2ZdK3UKkdxzWU56kYpQYsSli016f2PjuZZB1I3f+a90Hey7pF8ltyOctaV9o7m3Fm2vBZ+4RPRa4RR2kd0bLogesYyiJ4o7gkXPBa9oFSfSXDWn3SPonXsFPQ9Aqb/LnpBqTFfgGNu0j2K1rEs6VwXxaDUST+VTlMhBaPCWym2IwbpBY9L/XwU6FAGFz0NJX2i5/E2xUnJWC9+JjqPW3UfJnOXLoheZLQpdkoumZ77t+iUAg08Bc68Tw6aKBvLli0Zl1N+Hh0lMunaaCuPppz8U2lFGQXslDi2Q6ZF53fZErYt4zv3r3DQ0bpHI+SViBHabUg0nYwsdgpyi+ELz8j9pSjQ+SAZT15p9HVs2SC48PHoPDTcd7LoLcuJ/p4dPAVm/C46+2PMF+X8jQNH3Ride0KWJZf8x34x1DVn+X7IIVbPlVaQD50HHz4fqqg9UtpXlEdRZbQPwKa4Gdi5Hu6dCmsW6htH7wnw+b9KBC0qB8ve8DKw6h24/3TYvk7fOEaeJ3WtWNGM7rWE78G7j8CjX4FMrZ4xWLaUS038mh757eGfP4BXfqwve5VTKOUNBx8fbYOlKZ4r8zXzEnh3pr5xFFXAeY9Al/7xOevcDNRthT9NhxVz9I2jYqTMXW7H+OhYLwPrP4L7ToMtK/SNY9AUOP33sl+jHMhriu/Bhy/A3y6Gum16xmBZMOnrcPSNeuS3hzdug+e/o+/uXCoXptwmJd0hB0DDdzRAJtJ24N93wQs3Qd320EX+B8sSb/e4m0VxRLW2sDXcDODBP26G13+l9l5JKg+O/BZMuFLkxkXxZvEysGsrPHmNGM4qKewiNdSDp4oBFeVMRkt4LmxaCjMvheWz1MruMkCi8RWj4jdvWZa+Do9eDhs/USu3z+FysT+kWttQ8TxxxqsfgGf+G2o3qZU/4hyJ7qXy4mMoZ/Ea3gt45Sfw6k/UPkDrpCXzmH0bJW56wq2HzC74+/VQfb9a2XnFcPwt8pBmdv3HCc+Fbavgkcvh45fVyi7pDafdHs8H8UDsgppqeOQyWPuBWtmVo+XuXmlfJWtOjaORxXNhaw088mX45NXw5ZX0ljasfSbG09hriu/DyrnwyKWw7sPw5fUYIwuxpHf8DJam+J5Eid57DJ66Vk12Y/BpDW0dO8ZP6TbFy8jcvX6bROnDzm5YttSwHn0jYMfP2GuKWy/z99y3Yfbd4Wc3cgrhmO9J6VY2sBNXvAzs3AiPXQGLng1fXsdyyToefFzjeRFXfE+MlpmXwqr54cvrNlT0RNnA+M+bZcPiF+Dxr8KWleHL7H+MGMoFnWOuJxrOm1l3S4Q+7OyGZcHoL8DxP5AW+7HWExnAl0DyG78KP7uRypWOZhOvUho8VutoQOOinP0HibxsXh68jJwO8iDakd+KZxajNbLZjVd+DG/dGU7Er0M3KVcZd3k8sxitkS0veP47MO8v4NYFL6PLwdKuc/DU+BssTclmN565AT58Nhyjudd4yTpWjo53QKAp2eDGJ6/B8zfCireDl2HZcgfohFvimcVojayemPcgvPxD2PBR8DLS+VB1rpTopfLjbbA0xc2ABbz2SynP2LE+eBkFnWDcFTDparnQn6S5y9TCi9+DufdB/c7gZZT2lexP1efjHxRoSja78cw3YeHj4VRfVIySzk19D49/8Lgpvgc18+DZb8HS14L/vmVB/2NFT5T2U545U+9oZPEygCUlLbN+B5++0f5vdj4IRl4g3V9yCuT7SVmITfE8cHfB23+UP2vfb/83K0dL14bhZzW8D5AQxdEUz5Ofbcd6eOsOqP5z+yNXli3t9A69VGri3UxylG5TvIysifVL4I1fw4K/Qe3m9n0znS9G8vgrpL47qXPn1kuwY9lb8OYd8MHTUqrRHvJL5QG+8VdKw4gkGSxNyWbV3n9K9MTHL7ff0S3pJXenDr1USlcgOUGBpniuzF/1/aInaua1/5vdh8Hoi2X+7FQy11x2fdVuhll3wZz7JNDSHixLyhrHXgKDTklWEK8p2XNo83Jxcuc/CDs2tO+bTo50bRx3uQSksudp0sjqv5p5omMXPt5+Rze3SN6zmXAldO7fqMcVo8/RyJJdNJs+hQUzpTyopnrfapsLu0B5FVRUwYAToOdYbROphezPunIefPCUzNvKati2eu//bnFPmbfyKhhymizCpG7g5vh+Q8bBgo9ehiX/kLmrmbd349myZa7KR4hzNnSGdAhLqpHcnKyz5tXDwqfg09dkza1esPdD0UlD2WBZcz3HwpDpUvJzoOzZ7M9Zu0UCLMv/LXO39v3GGvvWyCkUI6+8SgyWg0+QqJRP/Oq620L2bNpSA+8+LJeea6ol07E3FZZf2qgn+h8jNd1JdcxaIns2rVkoxsvKapm7fQmyFFU0zt2gU2X/HihnHTSuk6WvSzObmmqxUXZu3PO/Z1mSuagYKX+GzpC5PGB0bEM2w/MkC/7xKzJ3q97Z+x1dOyXvsJSPkA6SQ6dLQOBAWXfZNVe3A957BJY16Ik17+797lUqTx7MLq+C3hNh4MniqPm+Vj2h39HI4vuibLObcNdWmdz1iyGzEzJ1sshS+dCxu9wh6NBN/tlsduRAURzN8TzZ2NlNuH2dlGlsXi5pYLceUjmyCEv7QuWoxkieWy/zlsSI3r7QfO1sWgYrZkvWo36nbPpUrrT1LRskmzhdIP/sgaI0WsPNNLYn9VzYsESUcO1midj7nqy53A7QfbgoDye9+14/EHHrRaFalpTxrX4PVjco4cwumdNUrrRarRwNnfo2zrPvHxgKtzWa7ru67RIgWLdI9mtmV4OeyIPCMtET2feIjJ4A322cux0bZL9uWipzl53XdD4U9xI9kX0F+oDXEy7gNwZEtqyE5bNh+5oGHZtp0BP50HmAOGY5hfLPGj3RUCXhiE7Y8DGsnAM7N8nc+Z7MXU4hdBsmejaVa/QENNMTGQlKrZoHu7Y1ZsXTeZK5qBgpQdDsPHtuZOYuOo5GS2QXGr5E7kBqTy3nwFUW+4rngZ9pnDeQubNTB66y2Fc8VxTyZ+bOatzwhtbJ3iPykfUG8l8icuBFGreezy46GtpdHsBOxb5g9ETbaU1PWKkDI1PWHprrCavh/xg9sXe8jBjDRk/sP1kdC03mL9rNU6LtaBgMBoPBYDAYDIZYYkIWBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCxzgaBoPBYDAYDAaDIXCMo2EwGAwGg8FgMBgCJ5qOhlv/2f/te/J3Xmb3f85z1Y0r6vj+7nMHrfxdnfzzBsHzWl93e/u7Ax0vs/ve9NyW/85t9ncHOm69rL2meJndz7WW/u5Ap6UzzK2XPdr875rP8YGM78vcNac13WH0xH/wPJ9697NryfN9Ms3+zvd3/+cOeFrTCbv9ndGxu9GaDZfZ9dm/a80G1ExK9wAAmRgnLYtuzXvwyb9g2Vuwshp2rIe6rY2HXSoXOveHrgMb//QaBx3KGr9zIJH9mXdthZVzYcUcqKmWuduy4rMKJZUHJb2gogrKq6ByDJQPg3TBgTl3XgbsFGxbCytmy/zVVEPNfNixrnHDWpbMUecBjXPXY4ysPSfd+J0DBd8XY852YNMyWD6rcc2tfgdqNzcaxbYDOR2gbJDMW0UV9BgLpf3AAnzAjma8IxQ8Fyxb5m/94oa5myfzt+Z9qNvWaCjbDuSXQvfhn527oorG71iWxh9GMdl95tbB6vca9my1zN36JZDZ2agnnDQUlkH5CJm3ilFQOQoKOouz6xxA+xUaz/e67bLeVrzduGc3L/uswZLKheIeDWtuJFSOlnnMKTwg9UTG9Ug5Nht31DF/2SbmLd/MghXyZ+22XdS7suYsC/JSDn26FDC0sphhlcVU9SxhYPciclL2f75zwNBUT2xZKWdddr+uegd2bmx0Mixb1lfXgU107Fix9SxLvmU7+n4W1TTVn1tqYNHf4eNXYeMnsGU57NjQOHd5xaITiirlT9lAGHA8dD6oQU9YMr8asXxfY7jCywAWvP8UzPkjLH0D6nfs/3csSxbl0NOh6lxZsL6fXAPG9wEf6mvhnYeg+n5Y9u+2fcuyod8RMm+Dp4DlJHtDe678fNvXwdz7oPrPsG5R276VyoOBJ8PI86HfZPl2kpVw1kBbvwTm3APzH4KtNW37Vn4pDJ0Boy4QIybpBkz251s+G+bcC+8+Aru2tO1bxT1g+Nkw+kIJHCTd0XUzcpZ/+DzM/RMseqbliPy+0G0IjDgHRp4HeSXJ1hPQGDFeMFP0xNJ/tS1DYVnQa4LoiaEzZC0n2NH1fB8L2FKb4W9vL+Pht1fwXk3b9mvasThqYBlnjOnJkYeU4ft+sh2O7Hm0eTm8/UeY/yBs+rRt38rtCINPg1EXQs+xB5CemAULHobFL8C6D9v2rZJeMGQajDgbygY32j4a0ONoeBlRHrPugjd+3XZjpSXSBTDsDDjqW1DQJXlGs+dKtP21X4rB0lZjpSUKu8DYS2Dc5eKsJc148X1Y9wG8+jN4d2awKcZO/WD8FTDyAlG+SToMPU+MsY9fgX/dCkteDPb7PcbChKtg0CkNEbAErTsvIwbZgpnw+q8kmhcUliWRq0nXQq/DGn9PScFtKMl7+/fw5m9g09Lgvp3KhWFnwqRroFNf7RG/wPEyULtF9OvsuyV6HBT5pTD6YphwpThrCdOxnu+zbMMO7nhpCY/MXcGuTHAlUD1K87l4Yh/OPaw3KdtKlsPhe7KPls+Cf/0cPng62LK77sNhwlfF0fX9ZGUls0G8Rc/Bqz9ue+C4NfofA8f/QDJG2d+TQtQ6GllFOO9BePYGKYsKi5wOcOQNMO4rIjfuizJrsLz1G3jx+1C/MzxZ+aVw4o9g+JnJMF6yddsv3CTzF+aS79QPpv0Geh4WngyVeBkphXr8q5J5DJNe42DanRKxT4Lx4nuwbjE8cqmU5YXJ0Blwys8hXRj/s873xYn66GV47CsSGQ0L24EJX4Mjvyn/O+4BgmzUcs498Oy3gw1ENSe3Ixz7PzDmYq3R0qDI3rP4+Qsf8tuXl5DxwtMT5cV5/Oj04Rw+oCu+72PFPTPkZqQa5emvS6Y7TMqrYPqd0GVAMgIEvi/ljM/eAEtfD0+OZYtNd/R3oWM3pXOnztFwM5CphSeukpSQKroPhym/kvKMuG5mz5X7FjMvhU/fUCd34Mkyd3nF8Y0y+75EWB65DDZ8pEamZcFhX4ZjviebOY7GS9bBfOdv8PR1wUZE90Q6H466UbJqcc1uZEt9/vVzeOmHbS/z2V86lMEpt8q+jWuAwM2AVwfP3CBlF6ooGyRObveh8TVevIyUhD76leCzjnui35Ew7Q65ExNTZ8Pzfd6v2co1D1bzweqtyuSeNbYnN506mBzHjmd2IxsdX/QMPH4VbFutRq6TA5Ovl2wufjz1RHbuXv0p/OPm3ZtYhEUqF464QbK5ivSEGkfD98VAnnlJuNGp1rBsOOrbcPjXG6NlceLfd8LzN7Xt/kp7yS+FU38Bg6bEa948D/Dg+e/Am3eo28RN6dQPTv+9OLtxUsDZ6NSjl8P7T+oZQ6/xcPrdUNgtXhF6NyNn3N8uhpVz9Ixh2Olw6q8glRMvBey5UjIw8xK5oKwa24GJ10jZLcTP4ah+AP5+fbhZjNbI7Qgn/FDuvsQI3/fxgVtfWMTt/ww3i9EaFcV5/OysKg7t0wnbjpOOzUi1wBNfk3sYOqgYCTN+D6W94nXWuRlwd8HDX5ISMx0cciLMuBuc3NB1rBpHY/Yf4Klr9Rh7TRl9kZQXYEXfaM46RM9+C964Te9YLAuO/18pQ4sD2QuQD54HHz6ndyzpfDjrfon6xSHC7GagdiPccyqsWah3LB3L4cInoLRvPJwNLyNzdu/UcMtC94WKkXD+o1JCGoe58z1Y+CQ8/EV1GaDWGDINpv9OHI047FmAl/8P/vm/ukcBk7/RWIYWcTzfx/fh6r/M5Yn5Ad4TbQNpx+LWs6o4cVg5dtRtE2gMRt13mnQw00lBJznrug2Jh7PhZaTj4APnqKuyaI2ywXD+I9KNL8TKi3AdDd+Xi2hPXxedXtzDTofpdxELZ+OJq+HtP+geRSNH3Qifu073KPaM54FXD3+aLm2So4CThjPugYNPiHZmw83AzvVw9/Gw8WPdoxEKOsFFT0tr4SgbzG4GVs0XJ0NHRLkluh4CX3hWos1RVsC+JyV6j345Om+FDDgWzv6LOBpRz2w8fxO8dqvuUTQy4atw3M26R7FHsk7GpffN5sWFa3QPBwDbgh+dPpzpo3pE29nwMrBrG/zhRHmOIArkdhRno6Iq2medm4ENS+D3x6srR94bRRXwpRegQ7fQ5i68E9TzGuu7o+JkgIzpkS9H38l49lvRcjIA/vE/8NZvo/X7bIrvAx48eG50nAyQ9PJfL5IxRcWQao6Xkfdq/nhKdJwMkH7h905peBMmeg8RAY3K40/To+NkAKz9AO6ZIm8kRHbduVLfHSUnA6SV7sNf1D2KvfPKT6LlZIB0V3vph7pHsVe+9pe5kXEyADwfvvHwOzz/7mpcDSVc+4Tnynly32nRcTJA3hH70wxpBRvVR2HdjNxhuefU6DgZIG+c3H9mw/24cKqOwnE03IzcyXjs8mgapfMflIuGUVJsWTxXSs10l0u1xjPfkIuGUdzMlgVPXy9GQtRw6+Av54jB3Pwl1Chg2fDA2W1/UyRMtq2RTIGXid554nvyUNy9U6OlPLKsmg8PnR/NTJqbgfUfihMexbP4vUfhhe9Fb82B7IV3H5XgTxR56RYJ6kXwrPN9n588+wFPai6XagnX8/nqA3P5cM3W3V4bjwS2I/s17C56baF2k5zD9dtDM5jbTLbS4v7T1V2Y3x9WL5C7cSGVigb/Vd+XCX3ksuhGIEG6mmz8JFoGs5eBbavguW/rHknr+D48dqV0ENN956Ypbr20w5x9t+6RtE7ddukcFrVSDM+Vfvufvql7JK2z8WPZF1HLRFo2PPX1YN8CCprFL0YzsGJZsh+avkodNV7/pVzqj5Iu8zx5I+Opa3SPZM88fZ20xo7Qusu4HgtWbOa3r2iujd8Dda7HNQ9WR6/lrZeRBzN133vcE9tWw5PXRO9ulW1LJU2UskDNWfh4aPe8wvltvPg9PV1D9of6HfDQBUCEolV2ShZj3TbdI9kzW2vg7/8VHYPZ9yVj8NgVukeydz59Q97yiIry9TLyENo/ol1TDYgT+clr0TH63IyU/ejquLI/PPdtUcJRWXe+B6/+RPrHRxnfk6BZpPSELW3id2zQPZI9s3OjBKUilE3zgWsemhfd0qQGFtZs5RcvLsKLSjbNc6XBxbMxuOi/4GFpLBGVILJbL8Ge9x7VPZK988qP5C2PgHVssJZi9kLkW78N9LOhsXqBOEVR2MyeC7N+J68vx4HqP8srllHYzJYlB2DUndssL35fWqBGoazAshuiyrW6R7J3fF9a7kahhMr3JFjx+FV6x7Gv7Noq7ytEwehzM3J/5JUf6x7JvrHuQ9mzutccyNpf8DAsfEL3SPaND56WB9wicNb5vs9Pn/uAxWsiHshr4PZ/LuGDVREpobIdOT9qN+seyb7x5NUSsI1CCZVlScl5HPB9yUQGrCeCdTQsS6LKUSqp2Rv/vhO2r9E/5l1b5M2HOPHk19Ae6fMysGyW2se92kv9TolI6u6O4WZg9u/lQcO4sGkp/PMHaF93li37NYr1tq3x0T9h3oP6M0JOSvSE7nHsD2/8Gta+rz8j5NaJIRAn/n699vI41/NZsnY7d70aoUYXeyHj+fzX3+bpf8jPzcCCmbD4Bb3j2B+2r4XnvqW/hMpzJfC+7kO949gfVr8Lc+4LNIgc3G/BrYf3HoNV7wT2SSVkdjVE1jTWQ2Zr5Ou26xtDW9iyEuY9oDerYaekBCNufPQS1MzXa7jYNrz2S33y28rsP0Cdhscrs/i+lBFU369vDG3ltZ/rfaney0j5m+7e+/uL78kLvjozQl5DYCDqJVPN2bkRZt2tNavh2Ba3v7Q48iVTzVmwYguvLV6nN6vhpOBfP9Mnv63Mf1AaiejMRO7aCi//SJ/8tvLKjwM1iYNzNJy01J7Hkbn36zXyvUy0LzHviTdu0/e+ge/Bho/hw2f1yG8vr92qz3Bx66X8YtNSPfLbQ902KTPUZbj4Hrx5u/6H5drCmoWw5B/65s5Oweu/0CO7vbz7iN4MlmXDmzHVsW/9Bp3BvPXbdvHEvJXa5LeH3768RF9Ww8vAx6/GL4AMouPe+LW+ahUvI5UWtZv0yG8Pm5dJyWNAWedgVq/vS2po2VuBfE459Ttg7r16IvNZgy9uUaosaz+A5bP1ROZ9X94aiULtdFt4/0npHqMDJx2vcrPmzL1XX+mZZUn3lbjy9h/1zd22NdFsP70veBmYc68eJ83LwJJ/xuceWnO2rJALsRp0bMb1eHDWMurdeOqJVxevY/UWTXfo7FT03vPaH+b9WZ9sOwXVMdYT784MLPsdkKPhSQlNnFkwU09k3knHswSjKXPu1dOByrLi0fGnNTK7ZPw6atW3rZbyrbiyfonczVHt4GYNvq2r1MoNkg/+rudSp5eRs073fbj2UP1nPU6anYq/nph7nxYdm3JsHp6zXLncoPB9eHDWMlwdF5t3bZOAWFzZtkbulqh2cH1f7jrE6W5Gcz5+RYLwARCMdWg78WjdtSdqqvVcWHPr4tNpqjUWv6DnfYM1C+Nt8IHMneqaebdeeqHH2eAD+PAZlF8Kt+z4luplcevEyVTtpNmp+GYzsmz4CDZpyirE6TJuSyz5h5Yzp2bzTpasjdn9x2a8vGgtjuqLzZ4r50SU37nZFz58Tv2lcN+VO8txJrMrsM6iwcx+3Q6JMMYZt16676g+CNcsjETrv3axZYX6V5HdeinZijs11epl2g6s1CA3aFZWq48uW3Zy5k5H565V89XLDJrls9Sf2ZuWxae1aGvUbYONau+EuZ5P9aeblMoMg/dWbsFTfpHdh5oIvgC+v9RUq6+4sFPxDwyAZLMCyEIGM/tRfu1wf/jkVbV9l5NiLAOsmKN27mxHj5EeNFtXwfZ1amVadjLmTsfP4HnxvBjZnJpq9U7axk+kC0vcqalG6cVmLxOvFtR7QoOT9s6KmDtowM56l6XrFWdl7FQygiqrFui5Qxrnsqksn7wayGfa72i49cmIUgEsfUNtDamdSobBB7ByjqQLVZGUyDKod9I8Vw7fuLN9nfouQBs/CqxuVSsrFUcq3QQZyyurFXeLs5KjJxQ7aY5tJcLRAJizbJP6NrdJWHeZWli/WK3MnZvkbbS4s3NTIKVzAeREHLn0kgQ2Kn7Mx7KgZp5amWGxslrtXQMvA2sSsu5WzoGDjiTo9zNbZf3ieLwEvi8snw0Hn6DG8EuSsbxzo7yDU1ShRp5lJScwoPrMTkqpI8jcKW7pnRRHY8GKzUyrqlQncNtq9dn2sFg+Czr1U2ejbPhIjZz2kM6X87+o8jP/6RdVQklvKCrHyi8NRFT7HQ3Ljs/9DMuGnEKZ4HTDf+YUQLpA/ntBF/Vjivtl5izbFP8ctVvif0kty7bVastYtsSzn3yLbF3VkElTYLz4HmyN0Uvge2PrKnWOhu2oPyPConaTnD2pXHUykzJ3ivVdxvXYtCNGL9DvgTVbdmHbCkv2kmKbgOhYVW3w3Qys11w2lVPYghNRiV/c4ER0LMfKK/rMv+K79dRlXLbUwcYd9axeXsvyDUs5bWQl+Tnts0+CsW5cBQZfn8PhsMsanYJ0QYOT0MRpSOeHP46gSUpkuV7xz5EUJwOgfqe6rl2+D/Xx7sDyGTI71cmyFMsLG9UlYKrPiDBx69Q6GkmZO8X6ri6jptRoZM8SyoryQpUxuKJo7/9QkCRlzYFaHYunxklL58PQ0//jSPjFPaGkF3TsjpXboeV/x/fxfNiVcdm6pZb123axakstyzfuZMeu1kvfXc/HaYeTG4yjoeKizeTroe/nwpejmrh3nMqi8n4GJGfeQCLlvq/mIPR9PRfjwsL3FT7YaCVv3anEzF3bScrcKX4zyFV0Nlw2+SBOGNo9dDm+72OpMpjdOjVylKCwY5ePmi5X6QKYett//ue+rArLsnAsKMhJUZCToltRHoMrivf67/le+6oGgnE0VER2HjgbcjrI4g/r8LAs+MYn4Xy7NZwcIAERZlvxWxBOjlp5YWKn1EVbLEv9ux1hYjsKI1V+wtad2bNtxlJ7zyAxc6cyCwSkFL2fcO1D1Vz/cLjn0JGHlPGLs0eGKuMzpMLN0CjFclDmbFioWec71sMPe4cvZ/qdWAOOa9cngnE0clpJ0wRJ3Xb5EyY6Sq/S+erfoAiDtOJDSbW8MEnnS4RURRTEstTsV1Wk8lHWxcZHokhJQfV5F8fS1tZQbDAnZu4UG685KTWOxo668LPEyt/RyEnYWacs8W1DXokaWbWbwpcRQCC0/bvQ96FDWbs/Ewk69VMvs7SPeplhoHru8orlTxLo1FdtaUTng9TJCpvO/dVdpLcdKO2rRpYKVO5ZNyPrPAkUVarPCiZl7hT/HI5t0aM0GU5a7y6FatvblvRSJytsOvVT1+3MTkGHbmpkqaDLwe3+RPsdDS8DFQrTeWFSXqWw3huplS+vUicvTMqr1Nd0lo9QKy8sKkerLWMpqoSA2tZpp6JKXemU7UCPMWpkhU1JL8hTeLnUspKjJyqq1Mpz6xOmJ9TeNxlamYyA1PDKYnX3MwByOyYnsNJjjNq2yl0GqJMVJnnFUNKz3Z9pv6PhpKHX+HZ/JhKUjwBP4WU131evtMJCtbGcFCfNsqD7CIX3DBpIwtyV9hVlqJKO3aGgs1qZYaD69287UJkQJ628Su2lZsuBilHq5IVJxUilZ1296zEsIY5GVc+SdnX+aRNJsE/ySyW4ppKiCihuv4Gune7DAvlMMAWMXQYko+67cozaS3dOCnqMVScvLCxbFqRSY9lPRkajUz/1tbBeJhlzp+tnSIKTVj5CeQcgCrsko6SgYqTay+C2DZUJcTQq1UaWHdtieAIcjU6FOaG3z90Nt97oibbi+9D3cPVyg6Z8RCBdKoNxNKwEHIR2CroPVS+3tI/6qGzQdBmg/rKinYKeh6qVGQa6yknivl9B5k61sexloDIBJUCVo5W/0Awko3yqcrQY/yop6KQ+Khs0HbpBh65KRdqWxYieJcoTxkGjJStjp5KRhawYqb49tJeBPpPUygyDilGBXCcI5rT0MnDQ0YF8ShsDjlXfSQTESRt4snq5QTJoip63GUp6QTcNzmGQDD5Nec0ydgoGHCevh8aZodPVvqgOsl+HzFArM2jyS+UBVBVdzpri1stZEWd6jROjXzW+B4NOVS83SAZPUf/+CFCUn+bQPhp+ZwFy4tDuai+Cg1Qo9B4vmcg4M2QaQZm6+4yThn5HqZUZNOl8GHiSVN60k2Bm307BqAvi3Z+/6jz1Bh+IkzbyfPVyg2TUheqNFpDfV9U56uUGRUEnOPjEQDbyfpPOj7fh0mu8OJqqQ5WWDd0GB1a7qoVhp6uPyIPoh6HT4+3gVp2rPouWZdSFeuQGhabxZ1yP00f30CI7CPLSNlOqKkg5GvasZcOwM9TLDYqyQVL+o+O8KyqPd9XFwFMCa+ce3OwXdJKBxZGiCjjkBD0Gn52SFFsALcS0cNBR0pVAR27aSYmTFte3DUaer8dBA8lAHXqZHtlBMPZL+gw+NwNjvqhHdhAceqk+2ak8GH62PvntIa9EjC4dAbWsgxvXO30Voxru8ak/71KOGOolBfEMhE6tqiQ/raHMMcuhl+rR70Ew5gt6Asgg+mncV/TIDoJDLw2s5Cy4Xe+5cPjXA/ucUg77sl75bj2Mv1LvGNrKxK+pr39sSm4RVH1en/y24qTld67rALcduafR8zA98ttDcQ9Jh+vKoDopWXNx7D7V/xgJauhycPFh4lXxNFzGfEHvC91uBiZcpU9+e5h4lb7AAPJC+HnjFLyiHDCWBZdPPkjZW3O7D8CWhiUDjtc1graTXypZNB0BZBD9NGhKPFsEV46SbExApcnBaRvbkYhF/5jd1ejYXaKjqmu9m+KkpQQobo/39RgL/Y7QO3e+D4dfG7+sxsgL5KFLnQaXm4Ejv6lPfls5/DrUPfPaCnYKJl6tdwz7i2XBETfoi/CBGC6lfWDo6frG0BbyimHClRodNMRgGnQKdBuibwxtoetAGDxVa2m1Y1t8aVJfivPjldU4eVg5fboUYuvUE15Gzg2da78tTLhKr20Ccidp8jf0jqEtHH1ToIGBYFeOl4FTfxmvVren/hIcDZfAd8OG0+6IT6QvlQfT79SbzQCpvezQHY7+jt5x7A/FPeC4/1H7OGRLOClxFKvO1TuO/aHv52DMxfoViO2I4Rmn7l2HXiYPV+mK8GXxPDjpJ1CotgNRuzj+FnE2dJ/PngfT7tS//vcV22nQE+ovgTenQ16K7546WPcw9plOhTncfNpQPN16wk7JPYc4lQGVV0m1hY7Oek1x0jDirHi1RK86V+yCAAMDwToadgo6lsMJ/xvoZ0Nj+Jlw8PH6FS/IGHpPkOxKHDjq21DSOxoKz3Zg3OXQe6LukewbU28X51a30QLi7Jz043i0zswphNN+o6fDWUv4Hky/S0+3uv2lUz849vu6RyHYtgSjTvm57pHsGwOOg5HnRuOsc1JyV+Pwa3WPZN+YeLVUOkRAx6Zsm2mjenDMoDLdQ9knbj5tKB1yU3qzGVksS4J5nfvrHsnecXLkXNad9c7ie3DmPfEIwHfoBif+X+Dd4YLPhdmO1MUdfELgnw6UDmVw0k8jEWn5D74Px94c/Zq+nofC+Cv0Rwua4mVg2m+jX0I1+iLoNzkSihcQBeLkwtTbdI9k7xz7fSl1jMq6s1NQ2k/KCqKMZcveiFLpg5OSrmdDI94qOK8ETrs9Os4tyO9x8jei39q7bBAc8c1IrTvX8/m/GcMjX0J10rDunDSsXE+nqdawnOidIy0x+RvQpX80AgMg4yjuCSf/VPdI9s6pt0IqP/DfcTgrxnOlDCiqqXHbgal3yIvMOtqetYZlyaKcfqfeS4d7Iq8Ept2lpR/6HrFT0j3shB/qHknrdO4PJ9yiv2SqOU5KuodFOZs24NiGu1QRcTKy2Lak6KOcTZt4tQQHotZ+3PMkq1HcU/dIWsayZHz5pdFbdz4w43fRbRWcLoDpv9M9it1wbIvigjS3TB8WiYRyS5QX53HLtOH6S6aa46TkscooZ9N6jYNJ10TPGbIdGHG2/Ikqoy+GQ4J5N6M54fw2bEe6AZ3/qBimUcKyYMpt0P+o6Hi8Tclu5tN/Hz3lli6A8x6Gkh7RnDvbgdEXRjPCXNwDLnoS7JxolEw1J1tCNXiq7pHsTs/D4Kz7oxVVborvw7l/he7DdY9kd6rOhWNu0j2KlrFtSBfCRU9FMyh13A+ku1kUzzonBV0GwDl/iV5QysmBs/8sGY2oZG6bkLJtThzanZsieF+jc2EOf7l0HIW5TjRKpppjWXDUjfJuWtToNhTOfVj3KFrH9+GUW8W+ixqDToVTfhZaEDQ8t89JQdlAOP8RuUQXBSxLLiGOOCd6Hm9TbEdeCz/tN9FxNtIF8PkHoWJkNBVvU474b4lqRIWiSjGmCrpGUvECjc7PjN/DISfqHUtTKkfDeTNlzUVlLzTHduSexoWPR6sj0NAZUhIXtchoU5xUw/54MlrOxlE3SnloFI29LHZKMmln3Rede0JODpzxR2naENX9CliWxUUT+vLfJwzUPZT/0Kkwhz9fMo7KkvxolUw1x/fh1F/A8LN0j6SRskFw4RPSpCaq686yJKt84RPResjv4BPg9D9IljSk887y/ZC1kJeBdYvhvqmwdVWoovaI7cgl3OFnRVt5NMX3YNEz8NeLILNL3zjySiSTUTEyupu4JV79Gbz4Pb1j6HwQXNhgREWtdKUlPA/w4dEvw/yH9I6l72Rxbp109J1bkLaxmZ1w3zRYPkvvWMZ8oaEm2IrHeedmYMty+OMpsHmZvnFYFpz4I72PGu4vngtLX4cHzoa6bfrGkVMomYw+h8dKT/zpzaXc+NgCrf54eXEeD1w6jh5RdzKy+L7slaevg3/fpXcslaOleiZdEN1AXlM8F9w6+OuFsOhZvWMZMh1m3CWB9xCD7+E7GiBKZPsaeOQy+PiV0MXtRpcB0oWgfES0Mxkt4bmwZiE8cimsfle9/N4TpJ1iUXk8jL3mLHoWnrhKj5M7/Cwx9lL58TgAs2SVyFu/hRe+C/U71Mq3UzDpaph8Q8O9pfgYLXiu/Hnxe/Dm7ervMuUWwfE/iGZpw95w68VQfvwqWPi4evnFPWHqryUaHwfnrCmeCxs/gZmXwIq31cuvGCV3Czv1i9d+BXzf562PN3DdX+exfONO5fKPH9KNH04fTse8VDycjObMvR+evQFqN6uVa9ny2PIx35U1Fyf7xPPAAp79Frx1h/qsc06hlIaOuVh0VMh2sRpHA+QgtB2Ycw88d6OaRWlZcNjlshAtJ17GXlOyD2y9/EP418/VvF2RLpB2duMuF3lx2sRNyUaZn/o6zH9QjcwO3SS1fMiJSjZxaHgubFkpTu7S19XILBskQYFuQ+I7byCKY8XbMnfrl6iRedBRDU04usR3v3qe3N1YMBOe/jrs2KBG7uiL4Pj/ldKfOGQeW8LLyJ557Rfw0i1qsuBOjpSqTrxa1nxMdWzG9ah3fW5+6j3uf+tTJTJLCtJ8b8oQplZV4nk+th0z5zaLl4Ed6+HRr8DiF9TI7NQPpv0Gehwav6BAc5bPhie+BqsXqJHXY6zcAS6qVBYUUOdoZPFc2LlBJvb9p8KT02UATPkV9BofngzV+J5kNR69HFa9E56cPpPEYFG4EEMla+x/8Hd46loxnsPAsmDYGdI2OS5p3L2RdTLfvAP++QPYtTUcOU6OvOSavcifhLlzM+A3ZDfe+m14AYK8Ejju+9JWPBvQiTteBmq3wJNXw3uPhSenpLfoiX6TGzN5ccfzYMMS0RNhlvBVjpZ7hF36xzso0IDv+1iWxRtL1nH9w/NZtiG87MbxQ7pxy/ThFOWnSEWp82VbyZ47c++TQPLOjeHIsVPSffDY78uai2tQoCluRs6dN2+Hl34YXvljXrEEBCZdLTaRwmCUekcDGhflijnw+q8kTR6UEu55mBgsA08SOUlYiE1xM2KEffg8vP7L4ErRLFsuBU26Ri4qxTmL0RrZzND8B+HNXwdXipbKk8cfJ14tdzKyUdkk4blQvxNm/Q7+fSdsWRHMd/NL5T7BuK9AQedkGHrN8X3YthreuA3m3BtcNrekN4z7srQldHKS4WA0JbuP1r4vmdwFM6W2OQjKq2D8lTB0eqwj8a2SPb8/fhVeuxWWvBhMeYZlSeZswtfEOUugnsi4HpZl8fQ7Ndz16kfMXx7Mfs1xbE4eXs6XJx/EId07xjuL0RrZ+wdz7oE3fwMbPw7mu7lFMOp8mPBVeRQ6iXiuZIbevF0ctu3rgvluUaU4Z4ddpu2yvB5HI0vW4dixQYy/BQ/Dqvn7l/K1U1Jm0fdzkv7u3F9qfZPmYDQn63Bs+lQ29aLnYO1C+dn3lVSezN2gU6UNZoeyRCqO3ciuj5XzYO698NFLEgHcn62QWySX44dOlyxGugDwExHZ2yPZ8oyPXoLqP8Onb8Dm5fv3jcKu4swOP1v6dsftHkZb8H3Al7W38Al45yGJNu9vaVBJb7k3VXUu9D38wNivWT1RuwXm/0UyHCur9y/yZ9mS5e53pGR+ug0+MPREdn1sqZGz7oO/S4Blfxw2J0f0xMEnyN2foopG/ZNgMq5HyrFZtHorD85axiuL1rJk7Ta8/VATBTkOwyqLOXFYOdNHVVKUl8b1fJykORjNya67pa/D3D/BJ/+CTUv37xv5pVLmM+xMGDylYa/GpLlFW/F9yTb4Pnz4rNjEi56Buu37951O/UTHDpkub1D5vlYdq9fRaEr20PdcMfpWzJHyoHWLIFMrB6Odlkf28jtBRZXU53UbIq39spcuk27oNSe7MG1H5nDNQlgxWxTxlhXitLl1MkepXDFUKqqgcqwoXts5MIyVlsheyLJsqNshTu6K2VAzTyIL9bUyN6k8SOdDl4Mb1t1YKOkl3zgQjJWWaLpmdm6S/bpyjtSZ1m6WPet7chE+t4PcvagYJeUWHcrk3zsAjJUWabpmttZIjW5Ntezduu0yd5Yt6y6vBMqHy9xVjIS8Ivn3DtQ9m50734ONS8VZq6mG9Ytlv2Zq5f+fyoXCMmkAUjkGug+VPXyg6gloXDNuBtZ90LDu5sHmT2Xu3DpxKtJ5cjG+vAp6jIEuh8g+PUDXnOf74INtW9TWuyys2UL1sk0sWLGZddvq2JVxqXd9clM2eWmHvl0KGVpZzMheJfTqVIBtWf9xWg44mpZy1m6BlXNFT6x6R/RGZmeDnsiTC8pdBzXo2DGNmYsDVcdm9aPvSbn3qndER6x9X4J72bMsp1DKokp6S0VPr3GQX/LZb2gmOo5Gc7wMsIdIZ/ZQNOyOWy8KoSXP3/eSWVIWBL4v6661ufFcROPo37iRY2/rKluHmvTMRVvY27py62XeDkTjeG/sbV1lA1RJjoK2lT2tK6MnWsX3fTKeT7oVx8H1fHzfPzAdi72xVx27F7vvQMbzwM+0bvdGeO6i62gYDAaDwWAwGAyG2GJcboPBYDAYDAaDwRA4xtEwGAwGg8FgMBgMgWMcDYPBYDAYDAaDwRA4xtEwGAwGg8FgMBgMgWMcDYPBYDAYDAaDwRA4xtEwGAwGg8FgMBgMgWMcDYPBYDAYDAaDwRA4xtEwGAwGg8FgMBgMgWMcDYPBYDAYDAaDwRA4xtEwGAwGg8FgMBgMgWMcDYPBYDAYDAaDwRA4xtEwGAwGg8FgMBgMgWMcDYPBYDAYDAaDwRA4xtEwGAwGg8FgMBgMgWMcDYPBYDAYDAaDwRA4/w9vZwV07N3tagAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating an Interactive Viewer\n",
    "Let's define a function that will display the molecules in a particular cell.  We'll use the Jupyter [interact](https://colab.research.google.com/github/jupyter-widgets/ipywidgets/blob/master/docs/source/examples/Using%20Interact.ipynb) widget to make an interactive tool.  Note that cells are numbered from 0, with cell (0,0) in the bottom left corner. "
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-06T11:36:07.272605Z",
     "start_time": "2025-05-06T11:36:07.222777Z"
    }
   },
   "source": [
    "@interact(x=range(0,x_dim), y=range(0,y_dim))\n",
    "def display_mols(x, y):\n",
    "    return mols2grid.display(act_df.query(\"morgan_X==@x and morgan_Y==@y\"),subset=[\"img\",\"ID\",\"label\"])"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "interactive(children=(Dropdown(description='x', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), value=0), Dropdown(des…"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2b6338b339114b5482551ea79a301dc9"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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